Ex5.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Ex5_mainLoss+Diag+Coins.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JwRTIXeJqab_"
      },
      "source": [
        "## Basic Setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z4Zqx3ZBOKnO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3ea9cd9b-b639-4326-dd73-ab14958c54ae"
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import torch\n",
        "import networkx as nx\n",
        "\n",
        "from platform import python_version\n",
        "print(\"Python Version\", python_version())\n",
        "print('PyTorch Version', torch.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python Version 3.7.12\n",
            "PyTorch Version 1.10.0+cu111\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "evkPHCXNqISe",
        "outputId": "9139cc51-0316-4189-e138-5343fcffcfb5"
      },
      "source": [
        "# Device\n",
        "DEVICE = 'cpu'\n",
        "if torch.cuda.is_available():\n",
        "    DEVICE = \"cuda\"\n",
        "print(\"device =\", DEVICE)\n",
        "# Reproducibility\n",
        "SEED = 3407\n",
        "import random\n",
        "random.seed(SEED)\n",
        "np.random.seed(SEED)\n",
        "torch.manual_seed(SEED)\n",
        "torch.cuda.manual_seed_all(SEED)\n",
        "# Parallel processing\n",
        "NUM_CORES = 20\n",
        "torch.set_num_threads(NUM_CORES)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "device = cpu\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sjVT4mJ8qLpp",
        "outputId": "2d41b878-c594-42bd-b8d6-723f2ed5c1f5"
      },
      "source": [
        "if DEVICE==\"cpu\":\n",
        "    !pip install -q torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.10.0+cpu.html\n",
        "else:\n",
        "    !pip install -q torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.10.0+cu111.html\n",
        "import torch_geometric as pyg\n",
        "print('PyTorch Geometric Version', pyg.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PyTorch Geometric Version 2.0.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ojccKMV1qdrh"
      },
      "source": [
        "## Prepare graph"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "th2rr-XwqW5z"
      },
      "source": [
        "def to_node_idx(x,y):\n",
        "    return (8*x) + y\n",
        "\n",
        "def to_coord(node_idx):\n",
        "    return node_idx//8, node_idx%8\n",
        "\n",
        "edge_index = [[],[]]\n",
        "for i in range(8):\n",
        "    for j in range(8):\n",
        "        if i<7:\n",
        "            edge_index[0].append(to_node_idx(i,j));    edge_index[1].append(to_node_idx(i+1,j))\n",
        "            edge_index[0].append(to_node_idx(i+1,j));    edge_index[1].append(to_node_idx(i,j))\n",
        "            if j>0:\n",
        "                edge_index[0].append(to_node_idx(i,j));    edge_index[1].append(to_node_idx(i+1,j-1))\n",
        "                edge_index[0].append(to_node_idx(i+1,j-1));    edge_index[1].append(to_node_idx(i,j))\n",
        "        if i>0:\n",
        "            edge_index[0].append(to_node_idx(i,j));    edge_index[1].append(to_node_idx(i-1,j))\n",
        "            edge_index[0].append(to_node_idx(i-1,j));    edge_index[1].append(to_node_idx(i,j))\n",
        "            if j<7:\n",
        "                edge_index[0].append(to_node_idx(i,j));    edge_index[1].append(to_node_idx(i-1,j+1))\n",
        "                edge_index[0].append(to_node_idx(i-1,j+1));    edge_index[1].append(to_node_idx(i,j))\n",
        "        if j<7:\n",
        "            edge_index[0].append(to_node_idx(i,j));    edge_index[1].append(to_node_idx(i,j+1))\n",
        "            edge_index[0].append(to_node_idx(i,j+1));    edge_index[1].append(to_node_idx(i,j))\n",
        "            if i<7:\n",
        "                edge_index[0].append(to_node_idx(i,j));    edge_index[1].append(to_node_idx(i+1,j+1))\n",
        "                edge_index[0].append(to_node_idx(i+1,j+1));    edge_index[1].append(to_node_idx(i,j))\n",
        "        if j>0:\n",
        "            edge_index[0].append(to_node_idx(i,j));    edge_index[1].append(to_node_idx(i,j-1))\n",
        "            edge_index[0].append(to_node_idx(i,j-1));    edge_index[1].append(to_node_idx(i,j))\n",
        "            if i>0:\n",
        "                edge_index[0].append(to_node_idx(i,j));    edge_index[1].append(to_node_idx(i-1,j-1))\n",
        "                edge_index[0].append(to_node_idx(i-1,j-1));    edge_index[1].append(to_node_idx(i,j))\n",
        "\n",
        "edge_index = torch.tensor(edge_index, dtype=torch.long).to(DEVICE)\n",
        "COINS = set([0, 7, 56, 63])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n41CppvVqmv9",
        "outputId": "bd74f761-f7e6-4943-f498-20a0a182978e"
      },
      "source": [
        "data = pyg.data.Data(edge_index = edge_index).to(DEVICE)\n",
        "NUM_NODES = data.num_nodes\n",
        "NUM_EDGES = data.num_edges\n",
        "print(\"No. of nodes =\", NUM_NODES)\n",
        "print(\"No. of edges =\", NUM_EDGES)\n",
        "print(\"edge_index.shape =\", data.edge_index.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No. of nodes = 64\n",
            "No. of edges = 840\n",
            "edge_index.shape = torch.Size([2, 840])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "RJVtbHoVqnr0",
        "outputId": "0609e3b4-18c5-4333-ecb0-eb7a0202c235"
      },
      "source": [
        "graph = pyg.utils.to_networkx(data)\n",
        "pos = {}\n",
        "for idx in range(64):\n",
        "    x,y = to_coord(idx)\n",
        "    pos[idx] = np.array([x,y])\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(8, 8))\n",
        "color = ['y' for i in range(64)]\n",
        "for coin in COINS:\n",
        "    color[coin] = 'r'\n",
        "nx.drawing.nx_pylab.draw_networkx(graph, node_color=color, pos=pos, node_size=750, ax=ax)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YM9i6NEEqno8"
      },
      "source": [
        "def plot_policy(policy):\n",
        "    u = [[0.75 for i in range(8)] for j in range(8)]\n",
        "    v = [[0.75 for i in range(8)] for j in range(8)]\n",
        "    for state in range(64):\n",
        "        x,y = to_coord(state)\n",
        "        action = np.argmax(policy[state])\n",
        "        if action==0:\n",
        "            u[y][x] = 0\n",
        "            v[y][x] = 0.75\n",
        "        elif action==1:\n",
        "            u[y][x] = 0.75\n",
        "            v[y][x] = 0.75\n",
        "        elif action==2:\n",
        "            u[y][x] = 0.75\n",
        "            v[y][x] = 0\n",
        "        elif action==3:\n",
        "            u[y][x] = 0.75\n",
        "            v[y][x] = -0.75\n",
        "        elif action==4:\n",
        "            u[y][x] = 0\n",
        "            v[y][x] = -0.75\n",
        "        elif action==5:\n",
        "            u[y][x] = -0.75\n",
        "            v[y][x] = -0.75\n",
        "        elif action==6:\n",
        "            u[y][x] = -0.75\n",
        "            v[y][x] = 0\n",
        "        else:\n",
        "            u[y][x] = -0.75\n",
        "            v[y][x] = 0.75\n",
        "\n",
        "    graph = pyg.utils.to_networkx(data)\n",
        "    pos = {}\n",
        "    for idx in range(64):\n",
        "        x,y = to_coord(idx)\n",
        "        pos[idx] = np.array([x,y])\n",
        "\n",
        "    fig, ax = plt.subplots(figsize=(8, 8))\n",
        "    color = ['y' for i in range(64)]\n",
        "    for coin in COINS:\n",
        "        color[coin] = 'r'\n",
        "    nx.drawing.nx_pylab.draw_networkx(graph, node_color=color, pos=pos, node_size=750, ax=ax, edgelist=[])\n",
        "    \n",
        "    X = np.arange(0,8,1)\n",
        "    Y = np.arange(0,8,1)\n",
        "\n",
        "    ax.quiver(X, Y, u, v, scale_units='xy', scale=1)\n",
        "    \n",
        "    ax.xaxis.set_ticks([])\n",
        "    ax.yaxis.set_ticks([])\n",
        "    ax.set_aspect('equal')\n",
        "    plt.grid()\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "49yosqtWNi7B"
      },
      "source": [
        "## 3. Define random walker and Initialize policy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2JWCtmMGjBfr"
      },
      "source": [
        "POLICY = {}\n",
        "for i in range(8):\n",
        "    for j in range(8):\n",
        "        u = to_node_idx(i,j)\n",
        "        POLICY[u] = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])\n",
        "        if j<7: # up\n",
        "            POLICY[u][0] = 1.0\n",
        "        if j<7 and i<7: # up-right\n",
        "            POLICY[u][1] = 1.0\n",
        "        if i<7: # right\n",
        "            POLICY[u][2] = 1.0\n",
        "        if i<7 and j>0: # down-right\n",
        "            POLICY[u][3] = 1.0\n",
        "        if j>0: # down\n",
        "            POLICY[u][4] = 1.0\n",
        "        if j>0 and i>0: # down-left\n",
        "            POLICY[u][5] = 1.0\n",
        "        if i>0: # left\n",
        "            POLICY[u][6] = 1.0\n",
        "        if j<7 and i>0: # up-left\n",
        "            POLICY[u][7] = 1.0\n",
        "        POLICY[u] /= sum(POLICY[u])\n",
        "\n",
        "Q = {}\n",
        "for i in range(8):\n",
        "    for j in range(8):\n",
        "        u = to_node_idx(i,j)\n",
        "        Q[u] = {}\n",
        "        if j<7: # up\n",
        "            Q[u][0] = torch.rand(1)\n",
        "        if j<7 and i<7: # up-right\n",
        "            Q[u][1] = torch.rand(1)\n",
        "        if i<7: # right\n",
        "            Q[u][2] = torch.rand(1)\n",
        "        if i<7 and j>0: # down-right\n",
        "            Q[u][3] = torch.rand(1)\n",
        "        if j>0: # down\n",
        "            Q[u][4] = torch.rand(1)\n",
        "        if j>0 and i>0: # down-left\n",
        "            Q[u][5] = torch.rand(1)\n",
        "        if i>0: # left\n",
        "            Q[u][6] = torch.rand(1)\n",
        "        if j<7 and i>0: # up-left\n",
        "            Q[u][7] = torch.rand(1)\n",
        "\n",
        "RET = {}\n",
        "for i in range(8):\n",
        "    for j in range(8):\n",
        "        u = to_node_idx(i,j)\n",
        "        RET[u] = [[], [], [], [], [], [], [], []]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R3eBev53qnmX"
      },
      "source": [
        "def sample_episode(walk_len, start_node=None):\n",
        "    if start_node==None:\n",
        "        start_node = np.random.randint(0,NUM_NODES)\n",
        "    states = []\n",
        "    actions = []\n",
        "    rewards = []\n",
        "    curr_state = start_node\n",
        "    while len(states) < walk_len:\n",
        "        states.append(curr_state)\n",
        "        if curr_state in COINS:\n",
        "            rewards.append(1.0)\n",
        "        else:\n",
        "            rewards.append(0.0)\n",
        "        actions.append(np.random.choice(a=[0,1,2,3,4,5,6,7], size=None, replace=True, p=POLICY[curr_state]))\n",
        "        if actions[-1]==0:      # up\n",
        "            curr_state = to_node_idx(to_coord(curr_state)[0], to_coord(curr_state)[1]+1)\n",
        "        elif actions[-1]==1:      # up-right\n",
        "            curr_state = to_node_idx(to_coord(curr_state)[0]+1, to_coord(curr_state)[1]+1)\n",
        "        elif actions[-1]==2:    # right\n",
        "            curr_state = to_node_idx(to_coord(curr_state)[0]+1, to_coord(curr_state)[1])\n",
        "        elif actions[-1]==3:      # down-right\n",
        "            curr_state = to_node_idx(to_coord(curr_state)[0]+1, to_coord(curr_state)[1]-1)\n",
        "        elif actions[-1]==4:    # down\n",
        "            curr_state = to_node_idx(to_coord(curr_state)[0], to_coord(curr_state)[1]-1)\n",
        "        elif actions[-1]==5:      # down-left\n",
        "            curr_state = to_node_idx(to_coord(curr_state)[0]-1, to_coord(curr_state)[1]-1)\n",
        "        elif actions[-1]==6:    # left\n",
        "            curr_state = to_node_idx(to_coord(curr_state)[0]-1, to_coord(curr_state)[1])\n",
        "        else:      # up-left\n",
        "            curr_state = to_node_idx(to_coord(curr_state)[0]-1, to_coord(curr_state)[1]+1)\n",
        "    return states, actions, rewards"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G_MlK2uxrJpM"
      },
      "source": [
        "## Build models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H6FJwSCOqnj-"
      },
      "source": [
        "# Set Hyperparameters\n",
        "EMBED_DIM = 512\n",
        "EPS = 1e-15\n",
        "INIT_LR1 = 0.1\n",
        "\n",
        "WALK_LEN = 128\n",
        "INIT_LR2 = 1e-3"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SgWVxsR1qnhh"
      },
      "source": [
        "class Node2Vec(torch.nn.Module):\n",
        "    def __init__(self, embedding_dim=EMBED_DIM, num_nodes=NUM_NODES, sparse=False):\n",
        "        super(Node2Vec, self).__init__()\n",
        "        self.embedding_dim = embedding_dim\n",
        "        self.embedding = torch.nn.Embedding(num_nodes, embedding_dim, sparse=sparse)\n",
        "        self.reset_parameters()\n",
        "\n",
        "\n",
        "    def reset_parameters(self):\n",
        "        self.embedding.reset_parameters()\n",
        "\n",
        "\n",
        "    def forward(self, batch=None):\n",
        "        emb = self.embedding.weight\n",
        "        return emb if batch is None else emb[batch]\n",
        "\n",
        "\n",
        "    def loss(self, pos_rw, neg_rw):\n",
        "        start, rest = pos_rw[:, 0], pos_rw[:, 1:].contiguous()\n",
        "        h_start = self.embedding(start).view(pos_rw.size(0), 1, self.embedding_dim)\n",
        "        h_rest = self.embedding(rest.view(-1)).view(pos_rw.size(0), -1, self.embedding_dim)\n",
        "        out = (h_start * h_rest).sum(dim=-1).view(-1)\n",
        "        pos_loss = -torch.log(torch.sigmoid(out) + EPS).mean()\n",
        "\n",
        "        start, rest = neg_rw[:, 0], neg_rw[:, 1:].contiguous()\n",
        "        h_start = self.embedding(start).view(neg_rw.size(0), 1, self.embedding_dim)\n",
        "        h_rest = self.embedding(rest.view(-1)).view(neg_rw.size(0), -1, self.embedding_dim)\n",
        "\n",
        "        out = (h_start * h_rest).sum(dim=-1).view(-1)\n",
        "        neg_loss = -torch.log(1 - torch.sigmoid(out) + EPS).mean()\n",
        "        return pos_loss + neg_loss\n",
        "\n",
        "model1 = Node2Vec().to(DEVICE)\n",
        "optimizer1 = torch.optim.Adam(model1.parameters(), INIT_LR1)\n",
        "platLR1 = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer1, mode='min', factor=0.5, patience=1, threshold=0.0001, \n",
        "                                                    threshold_mode='abs', cooldown=0, min_lr=0, eps=1e-10, verbose=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2DvylOvFqneX"
      },
      "source": [
        "def get_contexts(walks, size):\n",
        "    batch = torch.tensor([], dtype=torch.int, requires_grad=False).to(DEVICE)\n",
        "    for sequence in walks:\n",
        "        for i in range(0,len(sequence)-size):\n",
        "            batch = torch.cat((batch, sequence[i:i+size].reshape(1,size)), axis=0)\n",
        "    return batch"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J_ZEVaearYQI"
      },
      "source": [
        "class InferNet(torch.nn.Module):\n",
        "    def __init__ (self, input_len=EMBED_DIM):\n",
        "        super(InferNet, self).__init__()\n",
        "        self.input_size = input_len\n",
        "        self.lin1 = torch.nn.Linear(self.input_size,512)\n",
        "        self.lin4 = torch.nn.Linear(512,1)\n",
        "\n",
        "    def forward(self, X):\n",
        "        X = torch.tanh(self.lin1(X))\n",
        "        X = self.lin4(X)\n",
        "        return X\n",
        "\n",
        "model2 = InferNet().to(DEVICE)\n",
        "optimizer2 = torch.optim.Adam(model2.parameters(), INIT_LR2)\n",
        "platLR2 = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer2, mode='min', factor=0.5, patience=1, threshold=0.0001, \n",
        "                                                    threshold_mode='abs', cooldown=0, min_lr=0, eps=1e-10, verbose=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0tKwUWa8rYNv"
      },
      "source": [
        "mse = torch.nn.MSELoss(reduction='sum')\n",
        "def getLoss2(imm_rewards, rewards):\n",
        "    main_loss = mse(torch.sum(imm_rewards), torch.sum(rewards)) / WALK_LEN\n",
        "    return main_loss"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m7Q1o1QIrYA-"
      },
      "source": [
        "def printRewards(rewards):\n",
        "    matrix = np.array([[float(rewards[to_node_idx(i,j)][0]) for i in range(8)] for j in range(8)])\n",
        "    cm = sns.light_palette(\"blue\", as_cmap=True)\n",
        "    x=pd.DataFrame(matrix).iloc[::-1]\n",
        "    x=x.style.background_gradient(cmap=cm, axis=None)\n",
        "    display(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bqK-2Z1Ur6sT"
      },
      "source": [
        "## Train models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Zppsa_AgrYI4",
        "outputId": "04d8933b-2c94-422b-f909-6113f01c95ed"
      },
      "source": [
        "NUM_ITER = 50\n",
        "CONT_SIZE = 3\n",
        "NUM_NEG = 1\n",
        "losses1 = []\n",
        "losses2 = []\n",
        "# GAMMA = 0.5\n",
        "\n",
        "model2.eval()\n",
        "all_states = model1.forward(torch.tensor([i for i in range(NUM_NODES)])).reshape(NUM_NODES,EMBED_DIM)\n",
        "imm_rewards = model2.forward(all_states).reshape(NUM_NODES,1)\n",
        "printRewards(imm_rewards)\n",
        "\n",
        "for iter in range(1,NUM_ITER+1):\n",
        "    states, actions, rewards = [], [], []\n",
        "    for start in range(NUM_NODES):\n",
        "        s, a, r = sample_episode(WALK_LEN, start)\n",
        "        states.append(s)\n",
        "        actions.append(a)\n",
        "        rewards.append(r)\n",
        "    neg_samples = torch.randint(low=0, high=NUM_NODES, size=(NUM_NODES,(WALK_LEN*NUM_NEG)-1))\n",
        "    neg_samples = neg_samples.reshape(NUM_NODES, (WALK_LEN*NUM_NEG)-1)\n",
        "\n",
        "    states = torch.tensor(states).to(DEVICE)\n",
        "    actions = torch.tensor(actions).to(DEVICE)\n",
        "    rewards = torch.tensor(rewards).to(DEVICE)\n",
        "    neg_samples = torch.cat((states[:,0].reshape(NUM_NODES,1), neg_samples.to(DEVICE)), axis=1)\n",
        "\n",
        "    model1.train()\n",
        "    optimizer1.zero_grad()\n",
        "    pos = get_contexts(states, CONT_SIZE)\n",
        "    neg = get_contexts(neg_samples, CONT_SIZE)\n",
        "    loss1 = model1.loss(pos.to(DEVICE), neg.to(DEVICE))\n",
        "    loss1.backward()\n",
        "    optimizer1.step()\n",
        "    loss1 = float(loss1)\n",
        "    platLR1.step(loss1)\n",
        "    \n",
        "    model2.train()\n",
        "    loss2 = 0.0\n",
        "    for i in range(NUM_NODES):\n",
        "        optimizer2.zero_grad()\n",
        "        ep_states = model1.forward(states[i,:].reshape(WALK_LEN)).reshape(WALK_LEN,EMBED_DIM)\n",
        "        imm_rewards = model2.forward(ep_states).reshape(WALK_LEN,1)\n",
        "        L = getLoss2(imm_rewards, rewards[i].reshape(WALK_LEN,1))\n",
        "        L.backward()\n",
        "        optimizer2.step()\n",
        "        loss2 += float(L)\n",
        "    loss2 /= float(NUM_NODES)\n",
        "    platLR2.step(loss2)\n",
        "\n",
        "    # if len(losses1)>0:\n",
        "    #     loss1 = (GAMMA*loss1) + ((1-GAMMA)*losses1[-1])\n",
        "    # if len(losses2)>0:\n",
        "    #     loss2 = (GAMMA*loss2) + ((1-GAMMA)*losses2[-1])\n",
        "\n",
        "    print(\"iter : \"+str(iter)+\" |  loss1 : \"+str(np.around(loss1,6))+\" |  loss2 : \"+str(np.around(loss2,6)))\n",
        "    losses1.append(loss1)\n",
        "    losses2.append(loss2)\n",
        "\n",
        "    if iter in [10, 25, 50]:\n",
        "        model2.eval()\n",
        "        all_states = model1.forward(torch.tensor([i for i in range(NUM_NODES)])).reshape(NUM_NODES,EMBED_DIM)\n",
        "        imm_rewards = model2.forward(all_states).reshape(NUM_NODES,1)\n",
        "        printRewards(imm_rewards)"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "        }</style><table id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002\" class=\"dataframe\"><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >0</th>        <th class=\"col_heading level0 col1\" >1</th>        <th class=\"col_heading level0 col2\" >2</th>        <th class=\"col_heading level0 col3\" >3</th>        <th class=\"col_heading level0 col4\" >4</th>        <th class=\"col_heading level0 col5\" >5</th>        <th class=\"col_heading level0 col6\" >6</th>        <th class=\"col_heading level0 col7\" >7</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row0_col0\" class=\"data row0 col0\" >0.476522</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row0_col1\" class=\"data row0 col1\" >-0.047226</td>\n",
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              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row0_col5\" class=\"data row0 col5\" >-0.077897</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row0_col6\" class=\"data row0 col6\" >-0.056860</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row0_col7\" class=\"data row0 col7\" >-0.193182</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row1_col0\" class=\"data row1 col0\" >-0.217227</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row1_col1\" class=\"data row1 col1\" >0.449050</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row1_col2\" class=\"data row1 col2\" >-0.253179</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row1_col3\" class=\"data row1 col3\" >0.222002</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row1_col4\" class=\"data row1 col4\" >-0.027036</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row1_col5\" class=\"data row1 col5\" >0.246256</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row1_col6\" class=\"data row1 col6\" >-0.162212</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row1_col7\" class=\"data row1 col7\" >-0.286815</td>\n",
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              "                        <th id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row2_col0\" class=\"data row2 col0\" >0.506386</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row2_col1\" class=\"data row2 col1\" >0.082366</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row2_col2\" class=\"data row2 col2\" >0.306199</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row2_col3\" class=\"data row2 col3\" >-0.037394</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row2_col4\" class=\"data row2 col4\" >-0.165799</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row2_col5\" class=\"data row2 col5\" >0.143218</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row2_col6\" class=\"data row2 col6\" >0.138173</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row2_col7\" class=\"data row2 col7\" >-0.189759</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row3_col0\" class=\"data row3 col0\" >-0.488101</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row3_col1\" class=\"data row3 col1\" >0.006839</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row3_col2\" class=\"data row3 col2\" >0.022729</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row3_col3\" class=\"data row3 col3\" >0.290967</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row3_col4\" class=\"data row3 col4\" >-0.300160</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row3_col5\" class=\"data row3 col5\" >-0.069967</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row3_col6\" class=\"data row3 col6\" >-0.169547</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row3_col7\" class=\"data row3 col7\" >-0.211054</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row4_col0\" class=\"data row4 col0\" >0.007664</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row4_col1\" class=\"data row4 col1\" >-0.277316</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row4_col2\" class=\"data row4 col2\" >0.244599</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row4_col3\" class=\"data row4 col3\" >0.205027</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row4_col4\" class=\"data row4 col4\" >0.196127</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row4_col5\" class=\"data row4 col5\" >0.139028</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row4_col6\" class=\"data row4 col6\" >0.417372</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row4_col7\" class=\"data row4 col7\" >0.360891</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row5_col0\" class=\"data row5 col0\" >-0.194938</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row5_col1\" class=\"data row5 col1\" >0.070811</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row5_col2\" class=\"data row5 col2\" >-0.375516</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row5_col3\" class=\"data row5 col3\" >-0.089531</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row5_col4\" class=\"data row5 col4\" >-0.094787</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row5_col5\" class=\"data row5 col5\" >0.238499</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row5_col6\" class=\"data row5 col6\" >-0.115760</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row5_col7\" class=\"data row5 col7\" >-0.220435</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row6_col0\" class=\"data row6 col0\" >0.131425</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row6_col1\" class=\"data row6 col1\" >0.726761</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row6_col2\" class=\"data row6 col2\" >0.018675</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row6_col3\" class=\"data row6 col3\" >-0.322981</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row6_col4\" class=\"data row6 col4\" >0.216040</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row6_col5\" class=\"data row6 col5\" >0.199119</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row6_col6\" class=\"data row6 col6\" >0.626912</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row6_col7\" class=\"data row6 col7\" >-0.070740</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row7_col0\" class=\"data row7 col0\" >0.025977</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row7_col1\" class=\"data row7 col1\" >0.212268</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row7_col2\" class=\"data row7 col2\" >0.275828</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row7_col3\" class=\"data row7 col3\" >-0.270882</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row7_col4\" class=\"data row7 col4\" >-0.411361</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row7_col5\" class=\"data row7 col5\" >0.278527</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row7_col6\" class=\"data row7 col6\" >-0.187398</td>\n",
              "                        <td id=\"T_cbd3a2b4_4481_11ec_a97a_0242ac1c0002row7_col7\" class=\"data row7 col7\" >0.193604</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fe5c98bacd0>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "iter : 1 |  loss1 : 18.854073 |  loss2 : 10.933523\n",
            "iter : 2 |  loss1 : 13.683125 |  loss2 : 0.837256\n",
            "iter : 3 |  loss1 : 12.18894 |  loss2 : 0.341383\n",
            "iter : 4 |  loss1 : 11.846163 |  loss2 : 0.130703\n",
            "iter : 5 |  loss1 : 11.615489 |  loss2 : 0.070011\n",
            "iter : 6 |  loss1 : 11.182859 |  loss2 : 0.059662\n",
            "iter : 7 |  loss1 : 10.946642 |  loss2 : 0.054551\n",
            "iter : 8 |  loss1 : 10.730477 |  loss2 : 0.065727\n",
            "iter : 9 |  loss1 : 10.536671 |  loss2 : 0.061238\n",
            "iter : 10 |  loss1 : 10.206418 |  loss2 : 0.049412\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "        }#T_e50eee50_4481_11ec_a97a_0242ac1c0002row7_col0{\n",
              "            background-color:  #1010fe;\n",
              "            color:  #f1f1f1;\n",
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              "            background-color:  #6b6bfa;\n",
              "            color:  #000000;\n",
              "        }#T_e50eee50_4481_11ec_a97a_0242ac1c0002row7_col2{\n",
              "            background-color:  #f0f0f3;\n",
              "            color:  #000000;\n",
              "        }#T_e50eee50_4481_11ec_a97a_0242ac1c0002row7_col3{\n",
              "            background-color:  #6a6afa;\n",
              "            color:  #000000;\n",
              "        }#T_e50eee50_4481_11ec_a97a_0242ac1c0002row7_col4{\n",
              "            background-color:  #c3c3f5;\n",
              "            color:  #000000;\n",
              "        }#T_e50eee50_4481_11ec_a97a_0242ac1c0002row7_col5{\n",
              "            background-color:  #6e6ef9;\n",
              "            color:  #000000;\n",
              "        }</style><table id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002\" class=\"dataframe\"><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >0</th>        <th class=\"col_heading level0 col1\" >1</th>        <th class=\"col_heading level0 col2\" >2</th>        <th class=\"col_heading level0 col3\" >3</th>        <th class=\"col_heading level0 col4\" >4</th>        <th class=\"col_heading level0 col5\" >5</th>        <th class=\"col_heading level0 col6\" >6</th>        <th class=\"col_heading level0 col7\" >7</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row0_col0\" class=\"data row0 col0\" >0.371777</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row0_col1\" class=\"data row0 col1\" >0.181136</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row0_col2\" class=\"data row0 col2\" >-0.161641</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row0_col3\" class=\"data row0 col3\" >0.005344</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row0_col4\" class=\"data row0 col4\" >-0.125856</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row0_col5\" class=\"data row0 col5\" >-0.047767</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row0_col6\" class=\"data row0 col6\" >0.338106</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row0_col7\" class=\"data row0 col7\" >0.441961</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row1_col0\" class=\"data row1 col0\" >0.314470</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row1_col1\" class=\"data row1 col1\" >0.135425</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row1_col2\" class=\"data row1 col2\" >0.112563</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row1_col3\" class=\"data row1 col3\" >-0.027423</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row1_col4\" class=\"data row1 col4\" >-0.083049</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row1_col5\" class=\"data row1 col5\" >0.065049</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row1_col6\" class=\"data row1 col6\" >0.167300</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row1_col7\" class=\"data row1 col7\" >0.157708</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row2_col0\" class=\"data row2 col0\" >-0.216899</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row2_col1\" class=\"data row2 col1\" >-0.140498</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row2_col2\" class=\"data row2 col2\" >-0.118059</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row2_col3\" class=\"data row2 col3\" >-0.138401</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row2_col4\" class=\"data row2 col4\" >0.111996</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row2_col5\" class=\"data row2 col5\" >-0.024235</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row2_col6\" class=\"data row2 col6\" >-0.049786</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row2_col7\" class=\"data row2 col7\" >0.007343</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row3_col0\" class=\"data row3 col0\" >0.058704</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row3_col1\" class=\"data row3 col1\" >0.187768</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row3_col2\" class=\"data row3 col2\" >0.128218</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row3_col3\" class=\"data row3 col3\" >-0.035629</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row3_col4\" class=\"data row3 col4\" >0.002798</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row3_col5\" class=\"data row3 col5\" >-0.014145</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row3_col6\" class=\"data row3 col6\" >-0.176565</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row3_col7\" class=\"data row3 col7\" >-0.133968</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row4_col0\" class=\"data row4 col0\" >0.032210</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row4_col1\" class=\"data row4 col1\" >-0.141801</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row4_col2\" class=\"data row4 col2\" >0.100900</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row4_col3\" class=\"data row4 col3\" >-0.006209</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row4_col4\" class=\"data row4 col4\" >-0.048376</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row4_col5\" class=\"data row4 col5\" >0.032019</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row4_col6\" class=\"data row4 col6\" >0.086656</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row4_col7\" class=\"data row4 col7\" >-0.122566</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row5_col0\" class=\"data row5 col0\" >0.020778</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row5_col1\" class=\"data row5 col1\" >-0.121472</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row5_col2\" class=\"data row5 col2\" >0.037254</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row5_col3\" class=\"data row5 col3\" >-0.137331</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row5_col4\" class=\"data row5 col4\" >-0.007411</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row5_col5\" class=\"data row5 col5\" >0.037263</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row5_col6\" class=\"data row5 col6\" >0.154749</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row5_col7\" class=\"data row5 col7\" >0.067347</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row6_col0\" class=\"data row6 col0\" >0.186140</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row6_col1\" class=\"data row6 col1\" >0.152518</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row6_col2\" class=\"data row6 col2\" >-0.006933</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row6_col3\" class=\"data row6 col3\" >0.168674</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row6_col4\" class=\"data row6 col4\" >-0.026850</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row6_col5\" class=\"data row6 col5\" >-0.040919</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row6_col6\" class=\"data row6 col6\" >0.123759</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row6_col7\" class=\"data row6 col7\" >0.144463</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row7_col0\" class=\"data row7 col0\" >0.387414</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row7_col1\" class=\"data row7 col1\" >0.092287</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row7_col2\" class=\"data row7 col2\" >-0.346310</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row7_col3\" class=\"data row7 col3\" >0.094177</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row7_col4\" class=\"data row7 col4\" >-0.195784</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row7_col5\" class=\"data row7 col5\" >0.080088</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row7_col6\" class=\"data row7 col6\" >0.182400</td>\n",
              "                        <td id=\"T_e50eee50_4481_11ec_a97a_0242ac1c0002row7_col7\" class=\"data row7 col7\" >0.439578</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fe5b635ae10>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "iter : 11 |  loss1 : 10.105071 |  loss2 : 0.033313\n",
            "iter : 12 |  loss1 : 9.960142 |  loss2 : 0.015365\n",
            "iter : 13 |  loss1 : 10.023799 |  loss2 : 0.022225\n",
            "iter : 14 |  loss1 : 10.015869 |  loss2 : 0.018104\n",
            "iter : 15 |  loss1 : 9.71533 |  loss2 : 0.011661\n",
            "iter : 16 |  loss1 : 9.581655 |  loss2 : 0.015875\n",
            "iter : 17 |  loss1 : 9.396087 |  loss2 : 0.013213\n",
            "iter : 18 |  loss1 : 9.42003 |  loss2 : 0.010867\n",
            "iter : 19 |  loss1 : 9.275849 |  loss2 : 0.010292\n",
            "iter : 20 |  loss1 : 9.214088 |  loss2 : 0.006457\n",
            "iter : 21 |  loss1 : 9.250169 |  loss2 : 0.007129\n",
            "iter : 22 |  loss1 : 9.273163 |  loss2 : 0.006279\n",
            "iter : 23 |  loss1 : 9.130705 |  loss2 : 0.008638\n",
            "iter : 24 |  loss1 : 9.053638 |  loss2 : 0.008946\n",
            "iter : 25 |  loss1 : 9.410683 |  loss2 : 0.009398\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "            background-color:  #8484f8;\n",
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              "        }</style><table id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002\" class=\"dataframe\"><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >0</th>        <th class=\"col_heading level0 col1\" >1</th>        <th class=\"col_heading level0 col2\" >2</th>        <th class=\"col_heading level0 col3\" >3</th>        <th class=\"col_heading level0 col4\" >4</th>        <th class=\"col_heading level0 col5\" >5</th>        <th class=\"col_heading level0 col6\" >6</th>        <th class=\"col_heading level0 col7\" >7</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row0_col0\" class=\"data row0 col0\" >0.466532</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row0_col1\" class=\"data row0 col1\" >0.158169</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row0_col2\" class=\"data row0 col2\" >-0.169385</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row0_col3\" class=\"data row0 col3\" >0.019013</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row0_col4\" class=\"data row0 col4\" >-0.031849</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row0_col5\" class=\"data row0 col5\" >-0.102017</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row0_col6\" class=\"data row0 col6\" >0.248797</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row0_col7\" class=\"data row0 col7\" >0.526777</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row1_col0\" class=\"data row1 col0\" >0.211001</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row1_col1\" class=\"data row1 col1\" >0.155824</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row1_col2\" class=\"data row1 col2\" >0.028335</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row1_col3\" class=\"data row1 col3\" >0.067282</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row1_col4\" class=\"data row1 col4\" >0.001572</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row1_col5\" class=\"data row1 col5\" >0.046048</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row1_col6\" class=\"data row1 col6\" >0.088108</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row1_col7\" class=\"data row1 col7\" >0.013109</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row2_col0\" class=\"data row2 col0\" >-0.208588</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row2_col1\" class=\"data row2 col1\" >-0.066817</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row2_col2\" class=\"data row2 col2\" >-0.013647</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row2_col3\" class=\"data row2 col3\" >-0.087383</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row2_col4\" class=\"data row2 col4\" >0.055018</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row2_col5\" class=\"data row2 col5\" >0.040520</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row2_col6\" class=\"data row2 col6\" >-0.000781</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row2_col7\" class=\"data row2 col7\" >0.029645</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row3_col0\" class=\"data row3 col0\" >0.042620</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row3_col1\" class=\"data row3 col1\" >0.061253</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row3_col2\" class=\"data row3 col2\" >0.090711</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row3_col3\" class=\"data row3 col3\" >-0.009442</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row3_col4\" class=\"data row3 col4\" >-0.009101</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row3_col5\" class=\"data row3 col5\" >-0.031830</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row3_col6\" class=\"data row3 col6\" >-0.032557</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row3_col7\" class=\"data row3 col7\" >-0.012788</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row4_col0\" class=\"data row4 col0\" >-0.011143</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row4_col1\" class=\"data row4 col1\" >-0.053616</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row4_col2\" class=\"data row4 col2\" >0.077859</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row4_col3\" class=\"data row4 col3\" >-0.057752</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row4_col4\" class=\"data row4 col4\" >-0.004506</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row4_col5\" class=\"data row4 col5\" >0.000564</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row4_col6\" class=\"data row4 col6\" >0.033918</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row4_col7\" class=\"data row4 col7\" >-0.012534</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row5_col0\" class=\"data row5 col0\" >-0.087443</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row5_col1\" class=\"data row5 col1\" >-0.045028</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row5_col2\" class=\"data row5 col2\" >0.000043</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row5_col3\" class=\"data row5 col3\" >-0.051190</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row5_col4\" class=\"data row5 col4\" >-0.021302</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row5_col5\" class=\"data row5 col5\" >0.007373</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row5_col6\" class=\"data row5 col6\" >0.050021</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row5_col7\" class=\"data row5 col7\" >-0.044981</td>\n",
              "            </tr>\n",
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              "                        <th id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row6_col0\" class=\"data row6 col0\" >0.197642</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row6_col1\" class=\"data row6 col1\" >0.148969</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row6_col2\" class=\"data row6 col2\" >0.000491</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row6_col3\" class=\"data row6 col3\" >0.054963</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row6_col4\" class=\"data row6 col4\" >-0.031630</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row6_col5\" class=\"data row6 col5\" >-0.030082</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row6_col6\" class=\"data row6 col6\" >0.092307</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row6_col7\" class=\"data row6 col7\" >0.053919</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row7_col0\" class=\"data row7 col0\" >0.501835</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row7_col1\" class=\"data row7 col1\" >0.074127</td>\n",
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              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row7_col3\" class=\"data row7 col3\" >0.025399</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row7_col4\" class=\"data row7 col4\" >-0.052236</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row7_col5\" class=\"data row7 col5\" >0.004605</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row7_col6\" class=\"data row7 col6\" >0.144913</td>\n",
              "                        <td id=\"T_0b923532_4482_11ec_a97a_0242ac1c0002row7_col7\" class=\"data row7 col7\" >0.573925</td>\n",
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          "text": [
            "iter : 26 |  loss1 : 8.82815 |  loss2 : 0.005949\n",
            "iter : 27 |  loss1 : 9.114285 |  loss2 : 0.007736\n",
            "iter : 28 |  loss1 : 9.124572 |  loss2 : 0.009823\n",
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            "iter : 31 |  loss1 : 8.90794 |  loss2 : 0.007183\n",
            "iter : 32 |  loss1 : 8.877952 |  loss2 : 0.005308\n",
            "iter : 33 |  loss1 : 9.084883 |  loss2 : 0.006192\n",
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            "iter : 37 |  loss1 : 8.685324 |  loss2 : 0.007172\n",
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            "iter : 39 |  loss1 : 8.813309 |  loss2 : 0.005141\n",
            "iter : 40 |  loss1 : 8.839278 |  loss2 : 0.006247\n",
            "iter : 41 |  loss1 : 8.984371 |  loss2 : 0.005865\n",
            "iter : 42 |  loss1 : 8.931166 |  loss2 : 0.007291\n",
            "iter : 43 |  loss1 : 8.774843 |  loss2 : 0.006994\n",
            "iter : 44 |  loss1 : 8.914617 |  loss2 : 0.006451\n",
            "iter : 45 |  loss1 : 8.895617 |  loss2 : 0.005775\n",
            "iter : 46 |  loss1 : 9.024598 |  loss2 : 0.007027\n",
            "iter : 47 |  loss1 : 8.867477 |  loss2 : 0.007752\n",
            "iter : 48 |  loss1 : 8.628921 |  loss2 : 0.005144\n",
            "iter : 49 |  loss1 : 8.925558 |  loss2 : 0.00553\n",
            "iter : 50 |  loss1 : 8.641019 |  loss2 : 0.007144\n"
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              "        }#T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row2_col1{\n",
              "            background-color:  #ccccf5;\n",
              "            color:  #000000;\n",
              "        }#T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row2_col3,#T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row5_col0{\n",
              "            background-color:  #d1d1f4;\n",
              "            color:  #000000;\n",
              "        }#T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row2_col4,#T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row5_col6{\n",
              "            background-color:  #aeaef6;\n",
              "            color:  #000000;\n",
              "        }#T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row2_col5{\n",
              "            background-color:  #b0b0f6;\n",
              "            color:  #000000;\n",
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              "            background-color:  #babaf6;\n",
              "            color:  #000000;\n",
              "        }#T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row2_col7{\n",
              "            background-color:  #adadf6;\n",
              "            color:  #000000;\n",
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              "            background-color:  #a9a9f6;\n",
              "            color:  #000000;\n",
              "        }#T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row3_col1{\n",
              "            background-color:  #acacf6;\n",
              "            color:  #000000;\n",
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              "            background-color:  #a1a1f7;\n",
              "            color:  #000000;\n",
              "        }#T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row3_col3,#T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row5_col2,#T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row5_col4,#T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row5_col5{\n",
              "            background-color:  #b8b8f6;\n",
              "            color:  #000000;\n",
              "        }#T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row3_col4,#T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row3_col7,#T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row4_col0{\n",
              "            background-color:  #bdbdf5;\n",
              "            color:  #000000;\n",
              "        }#T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row3_col6,#T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row5_col3{\n",
              "            background-color:  #bebef5;\n",
              "            color:  #000000;\n",
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              "            background-color:  #c3c3f5;\n",
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              "            background-color:  #a0a0f7;\n",
              "            color:  #000000;\n",
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              "            background-color:  #c6c6f5;\n",
              "            color:  #000000;\n",
              "        }#T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row4_col4,#T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row4_col5,#T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row6_col2{\n",
              "            background-color:  #b5b5f6;\n",
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              "        }#T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row4_col7{\n",
              "            background-color:  #bbbbf5;\n",
              "            color:  #000000;\n",
              "        }#T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row5_col1{\n",
              "            background-color:  #c5c5f5;\n",
              "            color:  #000000;\n",
              "        }#T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row6_col0{\n",
              "            background-color:  #8181f8;\n",
              "            color:  #000000;\n",
              "        }#T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row6_col1{\n",
              "            background-color:  #8a8af8;\n",
              "            color:  #000000;\n",
              "        }#T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row6_col3{\n",
              "            background-color:  #a2a2f7;\n",
              "            color:  #000000;\n",
              "        }#T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row6_col4{\n",
              "            background-color:  #bcbcf5;\n",
              "            color:  #000000;\n",
              "        }#T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row6_col6{\n",
              "            background-color:  #9a9af7;\n",
              "            color:  #000000;\n",
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              "            background-color:  #1616fe;\n",
              "            color:  #f1f1f1;\n",
              "        }#T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row7_col2{\n",
              "            background-color:  #e5e5f3;\n",
              "            color:  #000000;\n",
              "        }#T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row7_col4{\n",
              "            background-color:  #c2c2f5;\n",
              "            color:  #000000;\n",
              "        }#T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row7_col5{\n",
              "            background-color:  #b7b7f6;\n",
              "            color:  #000000;\n",
              "        }#T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row7_col6{\n",
              "            background-color:  #8f8ff8;\n",
              "            color:  #000000;\n",
              "        }#T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row7_col7{\n",
              "            background-color:  #0000ff;\n",
              "            color:  #f1f1f1;\n",
              "        }</style><table id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002\" class=\"dataframe\"><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >0</th>        <th class=\"col_heading level0 col1\" >1</th>        <th class=\"col_heading level0 col2\" >2</th>        <th class=\"col_heading level0 col3\" >3</th>        <th class=\"col_heading level0 col4\" >4</th>        <th class=\"col_heading level0 col5\" >5</th>        <th class=\"col_heading level0 col6\" >6</th>        <th class=\"col_heading level0 col7\" >7</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row0_col0\" class=\"data row0 col0\" >0.494959</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row0_col1\" class=\"data row0 col1\" >0.157958</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row0_col2\" class=\"data row0 col2\" >-0.179499</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row0_col3\" class=\"data row0 col3\" >0.018408</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row0_col4\" class=\"data row0 col4\" >-0.034152</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row0_col5\" class=\"data row0 col5\" >-0.101989</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row0_col6\" class=\"data row0 col6\" >0.229860</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row0_col7\" class=\"data row0 col7\" >0.556974</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row1_col0\" class=\"data row1 col0\" >0.204485</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row1_col1\" class=\"data row1 col1\" >0.162525</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row1_col2\" class=\"data row1 col2\" >0.019214</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row1_col3\" class=\"data row1 col3\" >0.055757</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row1_col4\" class=\"data row1 col4\" >-0.005752</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row1_col5\" class=\"data row1 col5\" >0.009107</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row1_col6\" class=\"data row1 col6\" >0.078178</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row1_col7\" class=\"data row1 col7\" >0.003509</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row2_col0\" class=\"data row2 col0\" >-0.193061</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row2_col1\" class=\"data row2 col1\" >-0.068212</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row2_col2\" class=\"data row2 col2\" >-0.004270</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row2_col3\" class=\"data row2 col3\" >-0.088871</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row2_col4\" class=\"data row2 col4\" >0.028304</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row2_col5\" class=\"data row2 col5\" >0.023474</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row2_col6\" class=\"data row2 col6\" >-0.010173</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row2_col7\" class=\"data row2 col7\" >0.030585</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row3_col0\" class=\"data row3 col0\" >0.047121</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row3_col1\" class=\"data row3 col1\" >0.039835</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row3_col2\" class=\"data row3 col2\" >0.072067</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row3_col3\" class=\"data row3 col3\" >-0.002019</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row3_col4\" class=\"data row3 col4\" >-0.019012</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row3_col5\" class=\"data row3 col5\" >-0.032595</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row3_col6\" class=\"data row3 col6\" >-0.021360</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row3_col7\" class=\"data row3 col7\" >-0.019340</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row4_col0\" class=\"data row4 col0\" >-0.017160</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row4_col1\" class=\"data row4 col1\" >-0.040165</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row4_col2\" class=\"data row4 col2\" >0.074814</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row4_col3\" class=\"data row4 col3\" >-0.048578</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row4_col4\" class=\"data row4 col4\" >0.008015</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row4_col5\" class=\"data row4 col5\" >0.005658</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row4_col6\" class=\"data row4 col6\" >0.018243</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row4_col7\" class=\"data row4 col7\" >-0.012937</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row5_col0\" class=\"data row5 col0\" >-0.087785</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row5_col1\" class=\"data row5 col1\" >-0.045742</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row5_col2\" class=\"data row5 col2\" >-0.003795</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row5_col3\" class=\"data row5 col3\" >-0.022388</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row5_col4\" class=\"data row5 col4\" >-0.002607</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row5_col5\" class=\"data row5 col5\" >-0.001217</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row5_col6\" class=\"data row5 col6\" >0.028054</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row5_col7\" class=\"data row5 col7\" >-0.050908</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row6_col0\" class=\"data row6 col0\" >0.178900</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row6_col1\" class=\"data row6 col1\" >0.150717</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row6_col2\" class=\"data row6 col2\" >0.007352</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row6_col3\" class=\"data row6 col3\" >0.068730</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row6_col4\" class=\"data row6 col4\" >-0.015316</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row6_col5\" class=\"data row6 col5\" >-0.039743</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row6_col6\" class=\"data row6 col6\" >0.097318</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row6_col7\" class=\"data row6 col7\" >0.055125</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row7_col0\" class=\"data row7 col0\" >0.540823</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row7_col1\" class=\"data row7 col1\" >0.076178</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row7_col2\" class=\"data row7 col2\" >-0.154898</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row7_col3\" class=\"data row7 col3\" >0.011672</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row7_col4\" class=\"data row7 col4\" >-0.038076</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row7_col5\" class=\"data row7 col5\" >-0.000625</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row7_col6\" class=\"data row7 col6\" >0.134271</td>\n",
              "                        <td id=\"T_4b13e9d0_4482_11ec_a97a_0242ac1c0002row7_col7\" class=\"data row7 col7\" >0.613275</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fe5b634e210>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "NpSZTqB_rYGL",
        "outputId": "d926ec58-063a-49db-e984-04db3a0a9835"
      },
      "source": [
        "printRewards(rewards[:,0].reshape(NUM_NODES,1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "            background-color:  #0000ff;\n",
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              "            <tr>\n",
              "                        <th id=\"T_4b1aab3a_4482_11ec_a97a_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
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              "                        <td id=\"T_4b1aab3a_4482_11ec_a97a_0242ac1c0002row5_col3\" class=\"data row5 col3\" >0.000000</td>\n",
              "                        <td id=\"T_4b1aab3a_4482_11ec_a97a_0242ac1c0002row5_col4\" class=\"data row5 col4\" >0.000000</td>\n",
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              "                        <th id=\"T_4b1aab3a_4482_11ec_a97a_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
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              "                        <td id=\"T_4b1aab3a_4482_11ec_a97a_0242ac1c0002row7_col4\" class=\"data row7 col4\" >0.000000</td>\n",
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              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fe5b8393150>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "LjyGbLSKsa1K",
        "outputId": "72c4df54-f790-4947-9684-15668653741c"
      },
      "source": [
        "x = [i for i in range(1,NUM_ITER+1)]\n",
        "plt.figure(figsize=(8,8))\n",
        "plt.plot(x, losses1, 'r', label='Node2Vec Loss')\n",
        "plt.xlabel('iteration')\n",
        "plt.ylabel('loss')\n",
        "plt.grid()\n",
        "plt.legend()\n",
        "plt.title(\"Node2Vec Loss vs Iterations\")\n",
        "fig.savefig('Node2VecLoss_iter.png')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "V1gwNscPs3jJ",
        "outputId": "bba2e22c-8990-4375-fbfe-24a09ea452fa"
      },
      "source": [
        "x = [i for i in range(1,NUM_ITER+1)]\n",
        "plt.figure(figsize=(8,8))\n",
        "plt.plot(x, list(losses2), 'g', label='InferNet Loss')\n",
        "plt.xlabel('iteration')\n",
        "plt.ylabel('loss')\n",
        "plt.grid()\n",
        "plt.legend()\n",
        "plt.title(\"InferNet Loss vs Iterations\")\n",
        "fig.savefig('InferNetLoss_iter.png')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3UheoJv6s7E0"
      },
      "source": [
        "## Improve policy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LyLo7KEIrYDo"
      },
      "source": [
        "def improvePolicy(policy, Q, state, eps=0.1):\n",
        "    q = torch.tensor([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])\n",
        "    for a in [0, 1, 2, 3, 4, 5, 6, 7]:\n",
        "        if a in Q[state].keys():\n",
        "            q[a] = Q[state][a]\n",
        "\n",
        "    policy[state] = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])\n",
        "    idx = 0\n",
        "    _, indices = torch.sort(q, descending=True)\n",
        "    policy[state][indices[idx]] = (1.0-eps)\n",
        "    # if indices[0]==7:\n",
        "        # print(\"state =\", state)\n",
        "        # print(\"policy =\", policy[state])\n",
        "    while ((state+1)%8 == 0 and indices[idx]==0) or (((state+1)%8 == 0 or (state>=56 and state<=63)) and indices[idx]==1) or (state>=56 and state<=63 and indices[idx]==2) or (((state>=56 and state<=63) or state%8 == 0) and indices[idx]==3) or (state%8 == 0 and indices[idx]==4) or ((state%8 == 0 or (state>=0 and state<=7)) and indices[idx]==5) or (state>=0 and state<=7 and indices[idx]==6) or (((state+1)%8 == 0 or (state>=0 and state<=7)) and indices[idx]==7):\n",
        "        policy[state][indices[idx]] = 0.0\n",
        "        idx += 1\n",
        "        policy[state][indices[idx]] = (1.0-eps)\n",
        "\n",
        "    num_actions = 8.0\n",
        "    if (state+1)%8 == 0:\n",
        "        num_actions -= 1.0\n",
        "    if (state+1)%8 == 0 or (state>=56 and state<=63):\n",
        "        num_actions -= 1.0\n",
        "    if state>=56 and state<=63:\n",
        "        num_actions -= 1.0\n",
        "    if (state>=56 and state<=63) or state%8 == 0:\n",
        "        num_actions -= 1.0\n",
        "    if state%8 == 0:\n",
        "        num_actions -= 1.0\n",
        "    if state%8 == 0 or (state>=0 and state<=7):\n",
        "        num_actions -= 1.0\n",
        "    if state>=0 and state<=7:\n",
        "        num_actions -= 1.0\n",
        "    if (state+1)%8 == 0 or (state>=0 and state<=7):\n",
        "        num_actions -= 1.0\n",
        "\n",
        "    if not ((state+1)%8 == 0):\n",
        "        policy[state][0] += (eps/num_actions)\n",
        "    if not ((state+1)%8 == 0 or (state>=56 and state<=63)):\n",
        "        policy[state][1] += (eps/num_actions)\n",
        "    if not (state>=56 and state<=63):\n",
        "        policy[state][2] += (eps/num_actions)\n",
        "    if not ((state>=56 and state<=63) or state%8 == 0):\n",
        "        policy[state][3] += (eps/num_actions)\n",
        "    if state%8 != 0:\n",
        "        policy[state][4] += (eps/num_actions)\n",
        "    if not (state%8 == 0 or (state>=0 and state<=7)):\n",
        "        policy[state][5] += (eps/num_actions)\n",
        "    if not (state>=0 and state<=7):\n",
        "        policy[state][6] += (eps/num_actions)\n",
        "    if not ((state+1)%8 == 0 or (state>=0 and state<=7)):\n",
        "        policy[state][7] += (eps/num_actions)    \n",
        "    return policy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6P5N73szrYLY"
      },
      "source": [
        "def walk_freq():\n",
        "    freq = {}\n",
        "    for i in range(1000):\n",
        "        for start in range(NUM_NODES):\n",
        "            states, a, r = sample_episode(8, start)\n",
        "            for s in states:\n",
        "                if s not in freq.keys():\n",
        "                    freq[s] = 0\n",
        "                freq[s] += 1\n",
        "    matrix = np.array([[int(freq[to_node_idx(i,j)]) for i in range(8)] for j in range(8)])\n",
        "    cm = sns.light_palette(\"green\", as_cmap=True)\n",
        "    x=pd.DataFrame(matrix).iloc[::-1]\n",
        "    x=x.style.background_gradient(cmap=cm, axis=None)\n",
        "    display(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "ImEZzhAGs3eM",
        "outputId": "c5e9495f-7586-48d9-f38c-58c85802199f"
      },
      "source": [
        "plot_policy(POLICY)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RRSMeU2ms2x_"
      },
      "source": [
        "model1.eval()\n",
        "model2.eval()\n",
        "all_states = model1.forward(torch.tensor([i for i in range(NUM_NODES)])).reshape(NUM_NODES,EMBED_DIM)\n",
        "imm_rewards = model2.forward(all_states).reshape(NUM_NODES,1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Asl5Pz8WrX-S",
        "outputId": "ca1f513f-c11e-4dda-9ef1-f9a402d3fcd5"
      },
      "source": [
        "cum_reward = []\n",
        "epsilon = 0.98\n",
        "model2.eval()\n",
        "NUM_ITER = 300\n",
        "WALK_LEN = 5\n",
        "walk_freq()\n",
        "\n",
        "for iter in range(1,NUM_ITER+1):\n",
        "    states, actions, rewards = [], [], []\n",
        "    for start in range(NUM_NODES):\n",
        "        s, a, r = sample_episode(WALK_LEN, start)\n",
        "        states.append(s)\n",
        "        actions.append(a)\n",
        "        rewards.append(r)\n",
        "    \n",
        "    for i in range(NUM_NODES):\n",
        "        G = 0.0\n",
        "        for j in reversed(range(WALK_LEN-1)):\n",
        "            G += float(imm_rewards[states[i][j+1]])\n",
        "            s = states[i][j]\n",
        "            a = actions[i][j]\n",
        "            vis_prev = False\n",
        "            for k in range(j):\n",
        "                if states[i][k]==s and actions[i][k]==a:\n",
        "                    vis_prev = True\n",
        "                    break\n",
        "            if vis_prev:\n",
        "                continue\n",
        "            \n",
        "            RET[s][a].append(G)\n",
        "            Q[s][a] = np.mean(RET[s][a])\n",
        "            POLICY = improvePolicy(POLICY, Q, s, epsilon)\n",
        "\n",
        "    epsilon *= 0.98\n",
        "    rewards = torch.tensor(rewards).to(DEVICE)\n",
        "    \n",
        "    cm = float(torch.sum(rewards))/float(NUM_NODES)\n",
        "    # if len(cum_reward)>0:\n",
        "        # cm = (GAMMA*cm) + ((1-GAMMA)*cum_reward[-1])\n",
        "    cum_reward.append(cm)\n",
        "\n",
        "    print(\"iter : \"+str(iter)+\" |  cum_reward : \"+str(cum_reward[-1]))\n",
        "    if iter in [5, 10, 20, 30, 50, 75, 150, 300]:\n",
        "        walk_freq()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "                        <th id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row0_col0\" class=\"data row0 col0\" >4415</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row0_col1\" class=\"data row0 col1\" >6926</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row0_col2\" class=\"data row0 col2\" >6688</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row0_col3\" class=\"data row0 col3\" >6549</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row0_col4\" class=\"data row0 col4\" >6401</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row0_col5\" class=\"data row0 col5\" >6392</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row0_col6\" class=\"data row0 col6\" >6791</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row0_col7\" class=\"data row0 col7\" >4530</td>\n",
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              "                        <th id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row1_col0\" class=\"data row1 col0\" >6846</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row1_col1\" class=\"data row1 col1\" >10284</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row1_col2\" class=\"data row1 col2\" >9668</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row1_col3\" class=\"data row1 col3\" >9459</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row1_col4\" class=\"data row1 col4\" >9404</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row1_col5\" class=\"data row1 col5\" >9643</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row1_col6\" class=\"data row1 col6\" >10062</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row1_col7\" class=\"data row1 col7\" >6822</td>\n",
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              "                        <th id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row2_col0\" class=\"data row2 col0\" >6586</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row2_col1\" class=\"data row2 col1\" >9626</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row2_col2\" class=\"data row2 col2\" >9235</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row2_col3\" class=\"data row2 col3\" >8995</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row2_col4\" class=\"data row2 col4\" >8994</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row2_col5\" class=\"data row2 col5\" >9022</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row2_col6\" class=\"data row2 col6\" >9501</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row2_col7\" class=\"data row2 col7\" >6532</td>\n",
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              "                        <th id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row3_col0\" class=\"data row3 col0\" >6294</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row3_col1\" class=\"data row3 col1\" >9238</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row3_col2\" class=\"data row3 col2\" >8706</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row3_col3\" class=\"data row3 col3\" >8629</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row3_col4\" class=\"data row3 col4\" >8797</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row3_col5\" class=\"data row3 col5\" >9029</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row3_col6\" class=\"data row3 col6\" >9413</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row3_col7\" class=\"data row3 col7\" >6382</td>\n",
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              "                        <th id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row4_col0\" class=\"data row4 col0\" >6409</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row4_col1\" class=\"data row4 col1\" >9061</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row4_col2\" class=\"data row4 col2\" >8788</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row4_col3\" class=\"data row4 col3\" >8525</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row4_col4\" class=\"data row4 col4\" >8709</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row4_col5\" class=\"data row4 col5\" >8870</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row4_col6\" class=\"data row4 col6\" >9465</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row4_col7\" class=\"data row4 col7\" >6294</td>\n",
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              "                        <th id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row5_col0\" class=\"data row5 col0\" >6654</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row5_col1\" class=\"data row5 col1\" >9628</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row5_col2\" class=\"data row5 col2\" >9100</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row5_col3\" class=\"data row5 col3\" >8882</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row5_col4\" class=\"data row5 col4\" >8969</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row5_col5\" class=\"data row5 col5\" >9181</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row5_col6\" class=\"data row5 col6\" >9571</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row5_col7\" class=\"data row5 col7\" >6651</td>\n",
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              "                        <th id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row6_col0\" class=\"data row6 col0\" >7068</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row6_col1\" class=\"data row6 col1\" >10317</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row6_col2\" class=\"data row6 col2\" >9632</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row6_col3\" class=\"data row6 col3\" >9447</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row6_col4\" class=\"data row6 col4\" >9431</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row6_col5\" class=\"data row6 col5\" >9623</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row6_col6\" class=\"data row6 col6\" >10122</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row6_col7\" class=\"data row6 col7\" >6951</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row7_col0\" class=\"data row7 col0\" >4640</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row7_col1\" class=\"data row7 col1\" >7036</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row7_col2\" class=\"data row7 col2\" >6509</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row7_col3\" class=\"data row7 col3\" >6393</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row7_col4\" class=\"data row7 col4\" >6452</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row7_col5\" class=\"data row7 col5\" >6471</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row7_col6\" class=\"data row7 col6\" >6798</td>\n",
              "                        <td id=\"T_5a7e6422_4482_11ec_a97a_0242ac1c0002row7_col7\" class=\"data row7 col7\" >4494</td>\n",
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            "iter : 5 |  cum_reward : 0.265625\n"
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              "                <tr>\n",
              "                        <th id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row0_col0\" class=\"data row0 col0\" >7111</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row0_col1\" class=\"data row0 col1\" >8415</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row0_col2\" class=\"data row0 col2\" >6182</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row0_col3\" class=\"data row0 col3\" >6034</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row0_col4\" class=\"data row0 col4\" >5703</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row0_col5\" class=\"data row0 col5\" >6978</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row0_col6\" class=\"data row0 col6\" >8795</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row0_col7\" class=\"data row0 col7\" >7251</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row1_col0\" class=\"data row1 col0\" >7983</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row1_col1\" class=\"data row1 col1\" >11820</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row1_col2\" class=\"data row1 col2\" >9847</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row1_col3\" class=\"data row1 col3\" >8024</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row1_col4\" class=\"data row1 col4\" >8689</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row1_col5\" class=\"data row1 col5\" >9502</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row1_col6\" class=\"data row1 col6\" >11192</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row1_col7\" class=\"data row1 col7\" >9236</td>\n",
              "            </tr>\n",
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              "                        <th id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row2_col0\" class=\"data row2 col0\" >6451</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row2_col1\" class=\"data row2 col1\" >9201</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row2_col2\" class=\"data row2 col2\" >8910</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row2_col3\" class=\"data row2 col3\" >7591</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row2_col4\" class=\"data row2 col4\" >7920</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row2_col5\" class=\"data row2 col5\" >8373</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row2_col6\" class=\"data row2 col6\" >8860</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row2_col7\" class=\"data row2 col7\" >6737</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row3_col0\" class=\"data row3 col0\" >6807</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row3_col1\" class=\"data row3 col1\" >11353</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row3_col2\" class=\"data row3 col2\" >8536</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row3_col3\" class=\"data row3 col3\" >7406</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row3_col4\" class=\"data row3 col4\" >7033</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row3_col5\" class=\"data row3 col5\" >7422</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row3_col6\" class=\"data row3 col6\" >8066</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row3_col7\" class=\"data row3 col7\" >5575</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row4_col0\" class=\"data row4 col0\" >5925</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row4_col1\" class=\"data row4 col1\" >8473</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row4_col2\" class=\"data row4 col2\" >9985</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row4_col3\" class=\"data row4 col3\" >7532</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row4_col4\" class=\"data row4 col4\" >7781</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row4_col5\" class=\"data row4 col5\" >8026</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row4_col6\" class=\"data row4 col6\" >8407</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row4_col7\" class=\"data row4 col7\" >6160</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row5_col0\" class=\"data row5 col0\" >6077</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row5_col1\" class=\"data row5 col1\" >9047</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row5_col2\" class=\"data row5 col2\" >8310</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row5_col3\" class=\"data row5 col3\" >9094</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row5_col4\" class=\"data row5 col4\" >7501</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row5_col5\" class=\"data row5 col5\" >7711</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row5_col6\" class=\"data row5 col6\" >8552</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row5_col7\" class=\"data row5 col7\" >6480</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row6_col0\" class=\"data row6 col0\" >8376</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row6_col1\" class=\"data row6 col1\" >11229</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row6_col2\" class=\"data row6 col2\" >10045</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row6_col3\" class=\"data row6 col3\" >9244</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row6_col4\" class=\"data row6 col4\" >8000</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row6_col5\" class=\"data row6 col5\" >8435</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row6_col6\" class=\"data row6 col6\" >11237</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row6_col7\" class=\"data row6 col7\" >7547</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row7_col0\" class=\"data row7 col0\" >7108</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row7_col1\" class=\"data row7 col1\" >8102</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row7_col2\" class=\"data row7 col2\" >6156</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row7_col3\" class=\"data row7 col3\" >6349</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row7_col4\" class=\"data row7 col4\" >5483</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row7_col5\" class=\"data row7 col5\" >5986</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row7_col6\" class=\"data row7 col6\" >7924</td>\n",
              "                        <td id=\"T_68324b4c_4482_11ec_a97a_0242ac1c0002row7_col7\" class=\"data row7 col7\" >6715</td>\n",
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              "    </tbody></table>"
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              "<pandas.io.formats.style.Styler at 0x7fe6e5f56950>"
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        {
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          "name": "stdout",
          "text": [
            "iter : 6 |  cum_reward : 0.21875\n",
            "iter : 7 |  cum_reward : 0.296875\n",
            "iter : 8 |  cum_reward : 0.390625\n",
            "iter : 9 |  cum_reward : 0.296875\n",
            "iter : 10 |  cum_reward : 0.375\n"
          ]
        },
        {
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              "            background-color:  #a8d2a8;\n",
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              "            background-color:  #eaf2ea;\n",
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              "            background-color:  #7dbd7d;\n",
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              "            background-color:  #cbe4cb;\n",
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              "            background-color:  #b8dab8;\n",
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              "            background-color:  #e7f1e7;\n",
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              "            background-color:  #98ca98;\n",
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              "            background-color:  #90c790;\n",
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              "            background-color:  #6eb66e;\n",
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              "            background-color:  #008000;\n",
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              "            background-color:  #d9ead9;\n",
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              "            background-color:  #a1cfa1;\n",
              "            color:  #000000;\n",
              "        }#T_7659d8d4_4482_11ec_a97a_0242ac1c0002row7_col6{\n",
              "            background-color:  #61b061;\n",
              "            color:  #000000;\n",
              "        }#T_7659d8d4_4482_11ec_a97a_0242ac1c0002row7_col7{\n",
              "            background-color:  #68b368;\n",
              "            color:  #000000;\n",
              "        }</style><table id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002\" class=\"dataframe\"><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >0</th>        <th class=\"col_heading level0 col1\" >1</th>        <th class=\"col_heading level0 col2\" >2</th>        <th class=\"col_heading level0 col3\" >3</th>        <th class=\"col_heading level0 col4\" >4</th>        <th class=\"col_heading level0 col5\" >5</th>        <th class=\"col_heading level0 col6\" >6</th>        <th class=\"col_heading level0 col7\" >7</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row0_col0\" class=\"data row0 col0\" >10162</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row0_col1\" class=\"data row0 col1\" >8706</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row0_col2\" class=\"data row0 col2\" >5937</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row0_col3\" class=\"data row0 col3\" >6351</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row0_col4\" class=\"data row0 col4\" >5076</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row0_col5\" class=\"data row0 col5\" >5878</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row0_col6\" class=\"data row0 col6\" >11634</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row0_col7\" class=\"data row0 col7\" >10492</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row1_col0\" class=\"data row1 col0\" >11177</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row1_col1\" class=\"data row1 col1\" >13270</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row1_col2\" class=\"data row1 col2\" >9123</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row1_col3\" class=\"data row1 col3\" >9234</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row1_col4\" class=\"data row1 col4\" >7295</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row1_col5\" class=\"data row1 col5\" >9508</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row1_col6\" class=\"data row1 col6\" >13061</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row1_col7\" class=\"data row1 col7\" >9508</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row2_col0\" class=\"data row2 col0\" >6097</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row2_col1\" class=\"data row2 col1\" >8679</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row2_col2\" class=\"data row2 col2\" >9164</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row2_col3\" class=\"data row2 col3\" >6785</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row2_col4\" class=\"data row2 col4\" >8241</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row2_col5\" class=\"data row2 col5\" >7060</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row2_col6\" class=\"data row2 col6\" >8846</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row2_col7\" class=\"data row2 col7\" >7640</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row3_col0\" class=\"data row3 col0\" >4926</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row3_col1\" class=\"data row3 col1\" >9427</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row3_col2\" class=\"data row3 col2\" >10053</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row3_col3\" class=\"data row3 col3\" >6273</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row3_col4\" class=\"data row3 col4\" >5799</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row3_col5\" class=\"data row3 col5\" >6178</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row3_col6\" class=\"data row3 col6\" >6987</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row3_col7\" class=\"data row3 col7\" >5031</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row4_col0\" class=\"data row4 col0\" >4876</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row4_col1\" class=\"data row4 col1\" >8319</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row4_col2\" class=\"data row4 col2\" >7533</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row4_col3\" class=\"data row4 col3\" >5961</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row4_col4\" class=\"data row4 col4\" >5749</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row4_col5\" class=\"data row4 col5\" >6986</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row4_col6\" class=\"data row4 col6\" >6913</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row4_col7\" class=\"data row4 col7\" >5713</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row5_col0\" class=\"data row5 col0\" >5658</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row5_col1\" class=\"data row5 col1\" >8643</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row5_col2\" class=\"data row5 col2\" >6752</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row5_col3\" class=\"data row5 col3\" >5906</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row5_col4\" class=\"data row5 col4\" >6000</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row5_col5\" class=\"data row5 col5\" >8855</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row5_col6\" class=\"data row5 col6\" >8984</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row5_col7\" class=\"data row5 col7\" >5729</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row6_col0\" class=\"data row6 col0\" >10044</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row6_col1\" class=\"data row6 col1\" >10897</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row6_col2\" class=\"data row6 col2\" >9436</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row6_col3\" class=\"data row6 col3\" >6549</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row6_col4\" class=\"data row6 col4\" >6975</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row6_col5\" class=\"data row6 col5\" >8327</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row6_col6\" class=\"data row6 col6\" >14618</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row6_col7\" class=\"data row6 col7\" >8620</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row7_col0\" class=\"data row7 col0\" >9580</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row7_col1\" class=\"data row7 col1\" >9679</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row7_col2\" class=\"data row7 col2\" >5618</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row7_col3\" class=\"data row7 col3\" >5727</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row7_col4\" class=\"data row7 col4\" >4924</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row7_col5\" class=\"data row7 col5\" >7941</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row7_col6\" class=\"data row7 col6\" >10578</td>\n",
              "                        <td id=\"T_7659d8d4_4482_11ec_a97a_0242ac1c0002row7_col7\" class=\"data row7 col7\" >10312</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fe5cfc3fd90>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "iter : 11 |  cum_reward : 0.46875\n",
            "iter : 12 |  cum_reward : 0.328125\n",
            "iter : 13 |  cum_reward : 0.359375\n",
            "iter : 14 |  cum_reward : 0.3125\n",
            "iter : 15 |  cum_reward : 0.28125\n",
            "iter : 16 |  cum_reward : 0.375\n",
            "iter : 17 |  cum_reward : 0.484375\n",
            "iter : 18 |  cum_reward : 0.390625\n",
            "iter : 19 |  cum_reward : 0.421875\n",
            "iter : 20 |  cum_reward : 0.453125\n"
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              "            background-color:  #e4f0e4;\n",
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              "            background-color:  #52a852;\n",
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              "                <tr>\n",
              "                        <th id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row0_col0\" class=\"data row0 col0\" >16007</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row0_col1\" class=\"data row0 col1\" >9271</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row0_col2\" class=\"data row0 col2\" >4625</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row0_col3\" class=\"data row0 col3\" >3512</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row0_col4\" class=\"data row0 col4\" >3834</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row0_col5\" class=\"data row0 col5\" >5852</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row0_col6\" class=\"data row0 col6\" >17962</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row0_col7\" class=\"data row0 col7\" >17480</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row1_col0\" class=\"data row1 col0\" >15217</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row1_col1\" class=\"data row1 col1\" >14524</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row1_col2\" class=\"data row1 col2\" >8691</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row1_col3\" class=\"data row1 col3\" >5267</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row1_col4\" class=\"data row1 col4\" >5603</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row1_col5\" class=\"data row1 col5\" >11816</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row1_col6\" class=\"data row1 col6\" >15240</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row1_col7\" class=\"data row1 col7\" >10615</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row2_col0\" class=\"data row2 col0\" >5167</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row2_col1\" class=\"data row2 col1\" >7044</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row2_col2\" class=\"data row2 col2\" >7997</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row2_col3\" class=\"data row2 col3\" >5122</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row2_col4\" class=\"data row2 col4\" >10944</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row2_col5\" class=\"data row2 col5\" >7114</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row2_col6\" class=\"data row2 col6\" >6735</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row2_col7\" class=\"data row2 col7\" >7148</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row3_col0\" class=\"data row3 col0\" >3391</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row3_col1\" class=\"data row3 col1\" >6343</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row3_col2\" class=\"data row3 col2\" >8303</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row3_col3\" class=\"data row3 col3\" >5021</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row3_col4\" class=\"data row3 col4\" >4560</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row3_col5\" class=\"data row3 col5\" >4682</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row3_col6\" class=\"data row3 col6\" >4850</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row3_col7\" class=\"data row3 col7\" >3502</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row4_col0\" class=\"data row4 col0\" >3516</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row4_col1\" class=\"data row4 col1\" >5155</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row4_col2\" class=\"data row4 col2\" >7860</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row4_col3\" class=\"data row4 col3\" >4279</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row4_col4\" class=\"data row4 col4\" >4216</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row4_col5\" class=\"data row4 col5\" >5823</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row4_col6\" class=\"data row4 col6\" >5057</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row4_col7\" class=\"data row4 col7\" >3456</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row5_col0\" class=\"data row5 col0\" >5068</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row5_col1\" class=\"data row5 col1\" >8892</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row5_col2\" class=\"data row5 col2\" >4986</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row5_col3\" class=\"data row5 col3\" >4151</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row5_col4\" class=\"data row5 col4\" >4429</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row5_col5\" class=\"data row5 col5\" >8342</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row5_col6\" class=\"data row5 col6\" >9421</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row5_col7\" class=\"data row5 col7\" >5174</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row6_col0\" class=\"data row6 col0\" >14545</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row6_col1\" class=\"data row6 col1\" >12491</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row6_col2\" class=\"data row6 col2\" >6831</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row6_col3\" class=\"data row6 col3\" >4390</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row6_col4\" class=\"data row6 col4\" >6302</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row6_col5\" class=\"data row6 col5\" >7109</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row6_col6\" class=\"data row6 col6\" >16058</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row6_col7\" class=\"data row6 col7\" >14585</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row7_col0\" class=\"data row7 col0\" >15722</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row7_col1\" class=\"data row7 col1\" >10932</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row7_col2\" class=\"data row7 col2\" >4419</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row7_col3\" class=\"data row7 col3\" >3317</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row7_col4\" class=\"data row7 col4\" >3719</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row7_col5\" class=\"data row7 col5\" >7964</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row7_col6\" class=\"data row7 col6\" >12829</td>\n",
              "                        <td id=\"T_8400cc36_4482_11ec_a97a_0242ac1c0002row7_col7\" class=\"data row7 col7\" >17523</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fe5b6323ad0>"
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          },
          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "iter : 21 |  cum_reward : 0.546875\n",
            "iter : 22 |  cum_reward : 0.5625\n",
            "iter : 23 |  cum_reward : 0.671875\n",
            "iter : 24 |  cum_reward : 0.78125\n",
            "iter : 25 |  cum_reward : 0.546875\n",
            "iter : 26 |  cum_reward : 0.71875\n",
            "iter : 27 |  cum_reward : 0.609375\n",
            "iter : 28 |  cum_reward : 0.640625\n",
            "iter : 29 |  cum_reward : 0.6875\n",
            "iter : 30 |  cum_reward : 0.765625\n"
          ]
        },
        {
          "output_type": "display_data",
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              "            background-color:  #e7f1e7;\n",
              "            color:  #000000;\n",
              "        }#T_9241bb70_4482_11ec_a97a_0242ac1c0002row7_col5{\n",
              "            background-color:  #b6d9b6;\n",
              "            color:  #000000;\n",
              "        }#T_9241bb70_4482_11ec_a97a_0242ac1c0002row7_col6{\n",
              "            background-color:  #178b17;\n",
              "            color:  #000000;\n",
              "        }#T_9241bb70_4482_11ec_a97a_0242ac1c0002row7_col7{\n",
              "            background-color:  #088408;\n",
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              "        }</style><table id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002\" class=\"dataframe\"><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >0</th>        <th class=\"col_heading level0 col1\" >1</th>        <th class=\"col_heading level0 col2\" >2</th>        <th class=\"col_heading level0 col3\" >3</th>        <th class=\"col_heading level0 col4\" >4</th>        <th class=\"col_heading level0 col5\" >5</th>        <th class=\"col_heading level0 col6\" >6</th>        <th class=\"col_heading level0 col7\" >7</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row0_col0\" class=\"data row0 col0\" >23941</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row0_col1\" class=\"data row0 col1\" >9973</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row0_col2\" class=\"data row0 col2\" >3988</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row0_col3\" class=\"data row0 col3\" >2694</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row0_col4\" class=\"data row0 col4\" >3018</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row0_col5\" class=\"data row0 col5\" >5289</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row0_col6\" class=\"data row0 col6\" >23733</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row0_col7\" class=\"data row0 col7\" >23975</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row1_col0\" class=\"data row1 col0\" >21011</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row1_col1\" class=\"data row1 col1\" >18258</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row1_col2\" class=\"data row1 col2\" >8254</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row1_col3\" class=\"data row1 col3\" >4072</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row1_col4\" class=\"data row1 col4\" >4051</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row1_col5\" class=\"data row1 col5\" >12065</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row1_col6\" class=\"data row1 col6\" >17249</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row1_col7\" class=\"data row1 col7\" >8735</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row2_col0\" class=\"data row2 col0\" >4975</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row2_col1\" class=\"data row2 col1\" >6636</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row2_col2\" class=\"data row2 col2\" >11859</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row2_col3\" class=\"data row2 col3\" >4152</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row2_col4\" class=\"data row2 col4\" >6599</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row2_col5\" class=\"data row2 col5\" >5768</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row2_col6\" class=\"data row2 col6\" >5207</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row2_col7\" class=\"data row2 col7\" >6449</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row3_col0\" class=\"data row3 col0\" >2800</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row3_col1\" class=\"data row3 col1\" >6441</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row3_col2\" class=\"data row3 col2\" >7199</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row3_col3\" class=\"data row3 col3\" >3798</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row3_col4\" class=\"data row3 col4\" >3169</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row3_col5\" class=\"data row3 col5\" >3248</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row3_col6\" class=\"data row3 col6\" >3698</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row3_col7\" class=\"data row3 col7\" >2777</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row4_col0\" class=\"data row4 col0\" >2767</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row4_col1\" class=\"data row4 col1\" >3834</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row4_col2\" class=\"data row4 col2\" >4771</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row4_col3\" class=\"data row4 col3\" >3031</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row4_col4\" class=\"data row4 col4\" >3064</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row4_col5\" class=\"data row4 col5\" >4733</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row4_col6\" class=\"data row4 col6\" >3667</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row4_col7\" class=\"data row4 col7\" >2574</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row5_col0\" class=\"data row5 col0\" >4098</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row5_col1\" class=\"data row5 col1\" >6644</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row5_col2\" class=\"data row5 col2\" >3571</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row5_col3\" class=\"data row5 col3\" >3121</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row5_col4\" class=\"data row5 col4\" >3424</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row5_col5\" class=\"data row5 col5\" >6967</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row5_col6\" class=\"data row5 col6\" >7929</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row5_col7\" class=\"data row5 col7\" >3816</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row6_col0\" class=\"data row6 col0\" >16958</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row6_col1\" class=\"data row6 col1\" >11465</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row6_col2\" class=\"data row6 col2\" >5606</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row6_col3\" class=\"data row6 col3\" >4609</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row6_col4\" class=\"data row6 col4\" >5765</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row6_col5\" class=\"data row6 col5\" >6421</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row6_col6\" class=\"data row6 col6\" >15859</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row6_col7\" class=\"data row6 col7\" >9940</td>\n",
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              "                        <th id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row7_col0\" class=\"data row7 col0\" >19808</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row7_col1\" class=\"data row7 col1\" >10728</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row7_col2\" class=\"data row7 col2\" >3568</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row7_col3\" class=\"data row7 col3\" >2703</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row7_col4\" class=\"data row7 col4\" >2990</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row7_col5\" class=\"data row7 col5\" >7420</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row7_col6\" class=\"data row7 col6\" >21847</td>\n",
              "                        <td id=\"T_9241bb70_4482_11ec_a97a_0242ac1c0002row7_col7\" class=\"data row7 col7\" >23221</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
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            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fe5cfc3fc90>"
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          "name": "stdout",
          "text": [
            "iter : 31 |  cum_reward : 0.765625\n",
            "iter : 32 |  cum_reward : 0.65625\n",
            "iter : 33 |  cum_reward : 0.6875\n",
            "iter : 34 |  cum_reward : 0.703125\n",
            "iter : 35 |  cum_reward : 0.78125\n",
            "iter : 36 |  cum_reward : 0.6875\n",
            "iter : 37 |  cum_reward : 0.78125\n",
            "iter : 38 |  cum_reward : 0.65625\n",
            "iter : 39 |  cum_reward : 0.890625\n",
            "iter : 40 |  cum_reward : 0.765625\n",
            "iter : 41 |  cum_reward : 0.859375\n",
            "iter : 42 |  cum_reward : 0.8125\n",
            "iter : 43 |  cum_reward : 0.8125\n",
            "iter : 44 |  cum_reward : 0.921875\n",
            "iter : 45 |  cum_reward : 0.890625\n",
            "iter : 46 |  cum_reward : 1.015625\n",
            "iter : 47 |  cum_reward : 0.921875\n",
            "iter : 48 |  cum_reward : 0.9375\n",
            "iter : 49 |  cum_reward : 0.96875\n",
            "iter : 50 |  cum_reward : 0.921875\n"
          ]
        },
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              "            background-color:  #3c9d3c;\n",
              "            color:  #000000;\n",
              "        }#T_a061b78c_4482_11ec_a97a_0242ac1c0002row7_col1{\n",
              "            background-color:  #b8dab8;\n",
              "            color:  #000000;\n",
              "        }#T_a061b78c_4482_11ec_a97a_0242ac1c0002row7_col6{\n",
              "            background-color:  #2b952b;\n",
              "            color:  #000000;\n",
              "        }#T_a061b78c_4482_11ec_a97a_0242ac1c0002row7_col7{\n",
              "            background-color:  #148a14;\n",
              "            color:  #000000;\n",
              "        }</style><table id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002\" class=\"dataframe\"><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >0</th>        <th class=\"col_heading level0 col1\" >1</th>        <th class=\"col_heading level0 col2\" >2</th>        <th class=\"col_heading level0 col3\" >3</th>        <th class=\"col_heading level0 col4\" >4</th>        <th class=\"col_heading level0 col5\" >5</th>        <th class=\"col_heading level0 col6\" >6</th>        <th class=\"col_heading level0 col7\" >7</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row0_col0\" class=\"data row0 col0\" >36603</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row0_col1\" class=\"data row0 col1\" >9672</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row0_col2\" class=\"data row0 col2\" >2916</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row0_col3\" class=\"data row0 col3\" >1791</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row0_col4\" class=\"data row0 col4\" >2031</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row0_col5\" class=\"data row0 col5\" >4164</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row0_col6\" class=\"data row0 col6\" >31030</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row0_col7\" class=\"data row0 col7\" >33010</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row1_col0\" class=\"data row1 col0\" >31346</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row1_col1\" class=\"data row1 col1\" >19701</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row1_col2\" class=\"data row1 col2\" >6476</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row1_col3\" class=\"data row1 col3\" >2592</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row1_col4\" class=\"data row1 col4\" >2471</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row1_col5\" class=\"data row1 col5\" >9355</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row1_col6\" class=\"data row1 col6\" >15326</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row1_col7\" class=\"data row1 col7\" >7333</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row2_col0\" class=\"data row2 col0\" >4195</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row2_col1\" class=\"data row2 col1\" >6557</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row2_col2\" class=\"data row2 col2\" >12209</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row2_col3\" class=\"data row2 col3\" >2646</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row2_col4\" class=\"data row2 col4\" >3397</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row2_col5\" class=\"data row2 col5\" >3822</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row2_col6\" class=\"data row2 col6\" >3272</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row2_col7\" class=\"data row2 col7\" >4933</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row3_col0\" class=\"data row3 col0\" >1959</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row3_col1\" class=\"data row3 col1\" >3910</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row3_col2\" class=\"data row3 col2\" >5796</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row3_col3\" class=\"data row3 col3\" >2461</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row3_col4\" class=\"data row3 col4\" >1914</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row3_col5\" class=\"data row3 col5\" >1884</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row3_col6\" class=\"data row3 col6\" >2207</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row3_col7\" class=\"data row3 col7\" >1836</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row4_col0\" class=\"data row4 col0\" >1839</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row4_col1\" class=\"data row4 col1\" >2400</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row4_col2\" class=\"data row4 col2\" >3471</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row4_col3\" class=\"data row4 col3\" >1944</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row4_col4\" class=\"data row4 col4\" >1855</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row4_col5\" class=\"data row4 col5\" >1957</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row4_col6\" class=\"data row4 col6\" >2294</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row4_col7\" class=\"data row4 col7\" >1842</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row5_col0\" class=\"data row5 col0\" >3355</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row5_col1\" class=\"data row5 col1\" >5317</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row5_col2\" class=\"data row5 col2\" >2380</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row5_col3\" class=\"data row5 col3\" >1992</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row5_col4\" class=\"data row5 col4\" >1948</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row5_col5\" class=\"data row5 col5\" >4888</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row5_col6\" class=\"data row5 col6\" >5684</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row5_col7\" class=\"data row5 col7\" >4068</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row6_col0\" class=\"data row6 col0\" >25064</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row6_col1\" class=\"data row6 col1\" >9805</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row6_col2\" class=\"data row6 col2\" >5170</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row6_col3\" class=\"data row6 col3\" >2008</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row6_col4\" class=\"data row6 col4\" >2311</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row6_col5\" class=\"data row6 col5\" >5626</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row6_col6\" class=\"data row6 col6\" >14324</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row6_col7\" class=\"data row6 col7\" >9689</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row7_col0\" class=\"data row7 col0\" >27618</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row7_col1\" class=\"data row7 col1\" >9286</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row7_col2\" class=\"data row7 col2\" >2413</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row7_col3\" class=\"data row7 col3\" >1676</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row7_col4\" class=\"data row7 col4\" >1836</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row7_col5\" class=\"data row7 col5\" >5335</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row7_col6\" class=\"data row7 col6\" >30189</td>\n",
              "                        <td id=\"T_a061b78c_4482_11ec_a97a_0242ac1c0002row7_col7\" class=\"data row7 col7\" >33601</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fe5cfc3ad90>"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "iter : 51 |  cum_reward : 0.96875\n",
            "iter : 52 |  cum_reward : 1.09375\n",
            "iter : 53 |  cum_reward : 0.96875\n",
            "iter : 54 |  cum_reward : 0.96875\n",
            "iter : 55 |  cum_reward : 0.984375\n",
            "iter : 56 |  cum_reward : 1.09375\n",
            "iter : 57 |  cum_reward : 1.109375\n",
            "iter : 58 |  cum_reward : 1.125\n",
            "iter : 59 |  cum_reward : 1.1875\n",
            "iter : 60 |  cum_reward : 1.1875\n",
            "iter : 61 |  cum_reward : 1.296875\n",
            "iter : 62 |  cum_reward : 1.15625\n",
            "iter : 63 |  cum_reward : 1.09375\n",
            "iter : 64 |  cum_reward : 1.0625\n",
            "iter : 65 |  cum_reward : 1.25\n",
            "iter : 66 |  cum_reward : 1.125\n",
            "iter : 67 |  cum_reward : 1.203125\n",
            "iter : 68 |  cum_reward : 1.125\n",
            "iter : 69 |  cum_reward : 1.234375\n",
            "iter : 70 |  cum_reward : 1.296875\n",
            "iter : 71 |  cum_reward : 1.328125\n",
            "iter : 72 |  cum_reward : 1.296875\n",
            "iter : 73 |  cum_reward : 1.296875\n",
            "iter : 74 |  cum_reward : 1.265625\n",
            "iter : 75 |  cum_reward : 1.21875\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "            background-color:  #dcecdc;\n",
              "            color:  #000000;\n",
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              "            background-color:  #2c962c;\n",
              "            color:  #000000;\n",
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              "        }</style><table id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002\" class=\"dataframe\"><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >0</th>        <th class=\"col_heading level0 col1\" >1</th>        <th class=\"col_heading level0 col2\" >2</th>        <th class=\"col_heading level0 col3\" >3</th>        <th class=\"col_heading level0 col4\" >4</th>        <th class=\"col_heading level0 col5\" >5</th>        <th class=\"col_heading level0 col6\" >6</th>        <th class=\"col_heading level0 col7\" >7</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row0_col0\" class=\"data row0 col0\" >45057</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row0_col1\" class=\"data row0 col1\" >7178</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row0_col2\" class=\"data row0 col2\" >1940</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row0_col3\" class=\"data row0 col3\" >1356</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row0_col4\" class=\"data row0 col4\" >1482</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row0_col5\" class=\"data row0 col5\" >3006</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row0_col6\" class=\"data row0 col6\" >38295</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row0_col7\" class=\"data row0 col7\" >40735</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row1_col0\" class=\"data row1 col0\" >38915</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row1_col1\" class=\"data row1 col1\" >17346</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row1_col2\" class=\"data row1 col2\" >4717</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row1_col3\" class=\"data row1 col3\" >1777</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row1_col4\" class=\"data row1 col4\" >1704</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row1_col5\" class=\"data row1 col5\" >7585</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row1_col6\" class=\"data row1 col6\" >12529</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row1_col7\" class=\"data row1 col7\" >5388</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row2_col0\" class=\"data row2 col0\" >3103</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row2_col1\" class=\"data row2 col1\" >4716</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row2_col2\" class=\"data row2 col2\" >9887</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row2_col3\" class=\"data row2 col3\" >1752</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row2_col4\" class=\"data row2 col4\" >2648</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row2_col5\" class=\"data row2 col5\" >2885</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row2_col6\" class=\"data row2 col6\" >2050</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row2_col7\" class=\"data row2 col7\" >3948</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row3_col0\" class=\"data row3 col0\" >1371</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row3_col1\" class=\"data row3 col1\" >2866</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row3_col2\" class=\"data row3 col2\" >3936</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row3_col3\" class=\"data row3 col3\" >1694</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row3_col4\" class=\"data row3 col4\" >1437</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row3_col5\" class=\"data row3 col5\" >1380</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row3_col6\" class=\"data row3 col6\" >1543</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row3_col7\" class=\"data row3 col7\" >1355</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row4_col0\" class=\"data row4 col0\" >1381</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row4_col1\" class=\"data row4 col1\" >1596</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row4_col2\" class=\"data row4 col2\" >1470</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row4_col3\" class=\"data row4 col3\" >1403</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row4_col4\" class=\"data row4 col4\" >1408</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row4_col5\" class=\"data row4 col5\" >1431</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row4_col6\" class=\"data row4 col6\" >1528</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row4_col7\" class=\"data row4 col7\" >1391</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row5_col0\" class=\"data row5 col0\" >2521</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row5_col1\" class=\"data row5 col1\" >4053</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row5_col2\" class=\"data row5 col2\" >1631</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row5_col3\" class=\"data row5 col3\" >1411</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row5_col4\" class=\"data row5 col4\" >1468</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row5_col5\" class=\"data row5 col5\" >3963</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row5_col6\" class=\"data row5 col6\" >4261</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row5_col7\" class=\"data row5 col7\" >3011</td>\n",
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              "                        <th id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row6_col0\" class=\"data row6 col0\" >31631</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row6_col1\" class=\"data row6 col1\" >7894</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row6_col2\" class=\"data row6 col2\" >5267</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row6_col3\" class=\"data row6 col3\" >1509</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row6_col4\" class=\"data row6 col4\" >1621</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row6_col5\" class=\"data row6 col5\" >4272</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row6_col6\" class=\"data row6 col6\" >12054</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row6_col7\" class=\"data row6 col7\" >7758</td>\n",
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              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row7_col2\" class=\"data row7 col2\" >1840</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row7_col3\" class=\"data row7 col3\" >1393</td>\n",
              "                        <td id=\"T_ae933d30_4482_11ec_a97a_0242ac1c0002row7_col4\" class=\"data row7 col4\" >1421</td>\n",
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              "<pandas.io.formats.style.Styler at 0x7fe5cfc0d910>"
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          "name": "stdout",
          "text": [
            "iter : 76 |  cum_reward : 1.265625\n",
            "iter : 77 |  cum_reward : 1.203125\n",
            "iter : 78 |  cum_reward : 1.21875\n",
            "iter : 79 |  cum_reward : 1.234375\n",
            "iter : 80 |  cum_reward : 1.265625\n",
            "iter : 81 |  cum_reward : 1.3125\n",
            "iter : 82 |  cum_reward : 1.34375\n",
            "iter : 83 |  cum_reward : 1.296875\n",
            "iter : 84 |  cum_reward : 1.328125\n",
            "iter : 85 |  cum_reward : 1.375\n",
            "iter : 86 |  cum_reward : 1.515625\n",
            "iter : 87 |  cum_reward : 1.234375\n",
            "iter : 88 |  cum_reward : 1.28125\n",
            "iter : 89 |  cum_reward : 1.359375\n",
            "iter : 90 |  cum_reward : 1.34375\n",
            "iter : 91 |  cum_reward : 1.40625\n",
            "iter : 92 |  cum_reward : 1.375\n",
            "iter : 93 |  cum_reward : 1.34375\n",
            "iter : 94 |  cum_reward : 1.390625\n",
            "iter : 95 |  cum_reward : 1.390625\n",
            "iter : 96 |  cum_reward : 1.359375\n",
            "iter : 97 |  cum_reward : 1.40625\n",
            "iter : 98 |  cum_reward : 1.359375\n",
            "iter : 99 |  cum_reward : 1.390625\n",
            "iter : 100 |  cum_reward : 1.375\n",
            "iter : 101 |  cum_reward : 1.328125\n",
            "iter : 102 |  cum_reward : 1.25\n",
            "iter : 103 |  cum_reward : 1.421875\n",
            "iter : 104 |  cum_reward : 1.34375\n",
            "iter : 105 |  cum_reward : 1.359375\n",
            "iter : 106 |  cum_reward : 1.40625\n",
            "iter : 107 |  cum_reward : 1.390625\n",
            "iter : 108 |  cum_reward : 1.40625\n",
            "iter : 109 |  cum_reward : 1.46875\n",
            "iter : 110 |  cum_reward : 1.40625\n",
            "iter : 111 |  cum_reward : 1.453125\n",
            "iter : 112 |  cum_reward : 1.5\n",
            "iter : 113 |  cum_reward : 1.53125\n",
            "iter : 114 |  cum_reward : 1.46875\n",
            "iter : 115 |  cum_reward : 1.40625\n",
            "iter : 116 |  cum_reward : 1.5\n",
            "iter : 117 |  cum_reward : 1.515625\n",
            "iter : 118 |  cum_reward : 1.5\n",
            "iter : 119 |  cum_reward : 1.484375\n",
            "iter : 120 |  cum_reward : 1.4375\n",
            "iter : 121 |  cum_reward : 1.484375\n",
            "iter : 122 |  cum_reward : 1.359375\n",
            "iter : 123 |  cum_reward : 1.53125\n",
            "iter : 124 |  cum_reward : 1.453125\n",
            "iter : 125 |  cum_reward : 1.484375\n",
            "iter : 126 |  cum_reward : 1.515625\n",
            "iter : 127 |  cum_reward : 1.453125\n",
            "iter : 128 |  cum_reward : 1.46875\n",
            "iter : 129 |  cum_reward : 1.546875\n",
            "iter : 130 |  cum_reward : 1.453125\n",
            "iter : 131 |  cum_reward : 1.625\n",
            "iter : 132 |  cum_reward : 1.515625\n",
            "iter : 133 |  cum_reward : 1.53125\n",
            "iter : 134 |  cum_reward : 1.515625\n",
            "iter : 135 |  cum_reward : 1.40625\n",
            "iter : 136 |  cum_reward : 1.5625\n",
            "iter : 137 |  cum_reward : 1.5\n",
            "iter : 138 |  cum_reward : 1.5\n",
            "iter : 139 |  cum_reward : 1.546875\n",
            "iter : 140 |  cum_reward : 1.5625\n",
            "iter : 141 |  cum_reward : 1.5\n",
            "iter : 142 |  cum_reward : 1.5625\n",
            "iter : 143 |  cum_reward : 1.484375\n",
            "iter : 144 |  cum_reward : 1.5625\n",
            "iter : 145 |  cum_reward : 1.484375\n",
            "iter : 146 |  cum_reward : 1.59375\n",
            "iter : 147 |  cum_reward : 1.53125\n",
            "iter : 148 |  cum_reward : 1.515625\n",
            "iter : 149 |  cum_reward : 1.53125\n",
            "iter : 150 |  cum_reward : 1.625\n"
          ]
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              "                <tr>\n",
              "                        <th id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row0_col0\" class=\"data row0 col0\" >55335</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row0_col1\" class=\"data row0 col1\" >3295</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row0_col2\" class=\"data row0 col2\" >1146</td>\n",
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              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row0_col4\" class=\"data row0 col4\" >1076</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row0_col5\" class=\"data row0 col5\" >1472</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row0_col6\" class=\"data row0 col6\" >46733</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row0_col7\" class=\"data row0 col7\" >48832</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row1_col0\" class=\"data row1 col0\" >49073</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row1_col1\" class=\"data row1 col1\" >13308</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row1_col2\" class=\"data row1 col2\" >3282</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row1_col3\" class=\"data row1 col3\" >1127</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row1_col4\" class=\"data row1 col4\" >1101</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row1_col5\" class=\"data row1 col5\" >5621</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row1_col6\" class=\"data row1 col6\" >8252</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row1_col7\" class=\"data row1 col7\" >2131</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row2_col0\" class=\"data row2 col0\" >1510</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row2_col1\" class=\"data row2 col1\" >2600</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row2_col2\" class=\"data row2 col2\" >8302</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row2_col3\" class=\"data row2 col3\" >1123</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row2_col4\" class=\"data row2 col4\" >2111</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row2_col5\" class=\"data row2 col5\" >2140</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row2_col6\" class=\"data row2 col6\" >1183</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row2_col7\" class=\"data row2 col7\" >3141</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row3_col0\" class=\"data row3 col0\" >1074</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row3_col1\" class=\"data row3 col1\" >2157</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row3_col2\" class=\"data row3 col2\" >3139</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row3_col3\" class=\"data row3 col3\" >1098</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row3_col4\" class=\"data row3 col4\" >1064</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row3_col5\" class=\"data row3 col5\" >1068</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row3_col6\" class=\"data row3 col6\" >1098</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row3_col7\" class=\"data row3 col7\" >1058</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row4_col0\" class=\"data row4 col0\" >1060</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row4_col1\" class=\"data row4 col1\" >1099</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row4_col2\" class=\"data row4 col2\" >1089</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row4_col3\" class=\"data row4 col3\" >1055</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row4_col4\" class=\"data row4 col4\" >1058</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row4_col5\" class=\"data row4 col5\" >1088</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row4_col6\" class=\"data row4 col6\" >1078</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row4_col7\" class=\"data row4 col7\" >1073</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row5_col0\" class=\"data row5 col0\" >1382</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row5_col1\" class=\"data row5 col1\" >2484</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row5_col2\" class=\"data row5 col2\" >1106</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row5_col3\" class=\"data row5 col3\" >1076</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row5_col4\" class=\"data row5 col4\" >1068</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row5_col5\" class=\"data row5 col5\" >3151</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row5_col6\" class=\"data row5 col6\" >3212</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row5_col7\" class=\"data row5 col7\" >2152</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row6_col0\" class=\"data row6 col0\" >38145</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row6_col1\" class=\"data row6 col1\" >4158</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row6_col2\" class=\"data row6 col2\" >4208</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row6_col3\" class=\"data row6 col3\" >1076</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row6_col4\" class=\"data row6 col4\" >1101</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row6_col5\" class=\"data row6 col5\" >2501</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row6_col6\" class=\"data row6 col6\" >8242</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row6_col7\" class=\"data row6 col7\" >4120</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row7_col0\" class=\"data row7 col0\" >41259</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row7_col1\" class=\"data row7 col1\" >5937</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row7_col2\" class=\"data row7 col2\" >1116</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row7_col3\" class=\"data row7 col3\" >1057</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row7_col4\" class=\"data row7 col4\" >1059</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row7_col5\" class=\"data row7 col5\" >2479</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row7_col6\" class=\"data row7 col6\" >44817</td>\n",
              "                        <td id=\"T_bf08e34a_4482_11ec_a97a_0242ac1c0002row7_col7\" class=\"data row7 col7\" >49777</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
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              "<pandas.io.formats.style.Styler at 0x7fe5c99cb8d0>"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "iter : 151 |  cum_reward : 1.546875\n",
            "iter : 152 |  cum_reward : 1.46875\n",
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              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row0_col0\" class=\"data row0 col0\" >57861</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row0_col1\" class=\"data row0 col1\" >2065</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row0_col2\" class=\"data row0 col2\" >1008</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row0_col3\" class=\"data row0 col3\" >1002</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row0_col4\" class=\"data row0 col4\" >1007</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row0_col5\" class=\"data row0 col5\" >1027</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row0_col6\" class=\"data row0 col6\" >48902</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row0_col7\" class=\"data row0 col7\" >50901</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row1_col0\" class=\"data row1 col0\" >51837</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row1_col1\" class=\"data row1 col1\" >12070</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row1_col2\" class=\"data row1 col2\" >3009</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row1_col3\" class=\"data row1 col3\" >1009</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row1_col4\" class=\"data row1 col4\" >1004</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row1_col5\" class=\"data row1 col5\" >5034</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row1_col6\" class=\"data row1 col6\" >7062</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row1_col7\" class=\"data row1 col7\" >1053</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row2_col0\" class=\"data row2 col0\" >1023</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row2_col1\" class=\"data row2 col1\" >2038</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row2_col2\" class=\"data row2 col2\" >8021</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row2_col3\" class=\"data row2 col3\" >1006</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row2_col4\" class=\"data row2 col4\" >1998</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row2_col5\" class=\"data row2 col5\" >2006</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row2_col6\" class=\"data row2 col6\" >1010</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row2_col7\" class=\"data row2 col7\" >3008</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row3_col0\" class=\"data row3 col0\" >1005</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row3_col1\" class=\"data row3 col1\" >2004</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row3_col2\" class=\"data row3 col2\" >3012</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row3_col3\" class=\"data row3 col3\" >1005</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row3_col4\" class=\"data row3 col4\" >1003</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row3_col5\" class=\"data row3 col5\" >1004</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row3_col6\" class=\"data row3 col6\" >1005</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row3_col7\" class=\"data row3 col7\" >1005</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row4_col0\" class=\"data row4 col0\" >1003</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row4_col1\" class=\"data row4 col1\" >1005</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row4_col2\" class=\"data row4 col2\" >1005</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row4_col3\" class=\"data row4 col3\" >1004</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row4_col4\" class=\"data row4 col4\" >1004</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row4_col5\" class=\"data row4 col5\" >1005</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row4_col6\" class=\"data row4 col6\" >1003</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row4_col7\" class=\"data row4 col7\" >1002</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row5_col0\" class=\"data row5 col0\" >1023</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row5_col1\" class=\"data row5 col1\" >2017</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row5_col2\" class=\"data row5 col2\" >1004</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row5_col3\" class=\"data row5 col3\" >1003</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row5_col4\" class=\"data row5 col4\" >1003</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row5_col5\" class=\"data row5 col5\" >3007</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row5_col6\" class=\"data row5 col6\" >3001</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row5_col7\" class=\"data row5 col7\" >2010</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row6_col0\" class=\"data row6 col0\" >39925</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row6_col1\" class=\"data row6 col1\" >3048</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row6_col2\" class=\"data row6 col2\" >4000</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row6_col3\" class=\"data row6 col3\" >1004</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row6_col4\" class=\"data row6 col4\" >1005</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row6_col5\" class=\"data row6 col5\" >2026</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row6_col6\" class=\"data row6 col6\" >7050</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row6_col7\" class=\"data row6 col7\" >3062</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row7_col0\" class=\"data row7 col0\" >42929</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row7_col1\" class=\"data row7 col1\" >5045</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row7_col2\" class=\"data row7 col2\" >1003</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row7_col3\" class=\"data row7 col3\" >1000</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row7_col4\" class=\"data row7 col4\" >1004</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row7_col5\" class=\"data row7 col5\" >2026</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row7_col6\" class=\"data row7 col6\" >46898</td>\n",
              "                        <td id=\"T_d3488efa_4482_11ec_a97a_0242ac1c0002row7_col7\" class=\"data row7 col7\" >51872</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fe5cfb93e90>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o2FN_u_3tnAp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "3bd4d5c9-44cc-4914-ce65-628e1397d43b"
      },
      "source": [
        "x = [i for i in range(1,len(cum_reward)+1)]\n",
        "plt.figure(figsize=(8,8))\n",
        "plt.plot(x, cum_reward, 'r', label='Average Cumulative Reward')\n",
        "plt.xlabel('iteration')\n",
        "plt.ylabel('reward')\n",
        "plt.grid()\n",
        "plt.legend()\n",
        "plt.title(\"Average Cumulative Reward per episode vs Iterations\")\n",
        "fig.savefig('CumReward_iter.png')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U5Xk_sditm95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "04f5df9b-8d18-4ce9-c9fc-c3bca67ceba3"
      },
      "source": [
        "plot_policy(POLICY)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ZjsfBUNtm7S"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}