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      "execution_count": 1,
      "metadata": {
        "colab": {
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        },
        "id": "OTnjdK9eucNw",
        "outputId": "f8623a79-0e9a-4a65-9416-ae7148e7250c"
      },
      "outputs": [
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'coup'...\n",
            "remote: Enumerating objects: 260, done.\u001b[K\n",
            "remote: Counting objects: 100% (260/260), done.\u001b[K\n",
            "remote: Compressing objects: 100% (193/193), done.\u001b[K\n",
            "remote: Total 260 (delta 148), reused 157 (delta 62), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (260/260), 3.24 MiB | 4.45 MiB/s, done.\n",
            "Resolving deltas: 100% (148/148), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/okg21/coup.git\n",
        "!cd coup"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install wandb"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yC_ZA-a2krGy",
        "outputId": "602ff882-ddab-43c2-eb2b-b469f76e0708"
      },
      "execution_count": 2,
      "outputs": [
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting wandb\n",
            "  Downloading wandb-0.16.1-py3-none-any.whl (2.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m15.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: Click!=8.0.0,>=7.1 in /usr/local/lib/python3.10/dist-packages (from wandb) (8.1.7)\n",
            "Collecting GitPython!=3.1.29,>=1.0.0 (from wandb)\n",
            "  Downloading GitPython-3.1.40-py3-none-any.whl (190 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190.6/190.6 kB\u001b[0m \u001b[31m17.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: requests<3,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from wandb) (2.31.0)\n",
            "Requirement already satisfied: psutil>=5.0.0 in /usr/local/lib/python3.10/dist-packages (from wandb) (5.9.5)\n",
            "Collecting sentry-sdk>=1.0.0 (from wandb)\n",
            "  Downloading sentry_sdk-1.39.0-py2.py3-none-any.whl (254 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m254.0/254.0 kB\u001b[0m \u001b[31m21.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting docker-pycreds>=0.4.0 (from wandb)\n",
            "  Downloading docker_pycreds-0.4.0-py2.py3-none-any.whl (9.0 kB)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from wandb) (6.0.1)\n",
            "Collecting setproctitle (from wandb)\n",
            "  Downloading setproctitle-1.3.3-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (30 kB)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from wandb) (67.7.2)\n",
            "Requirement already satisfied: appdirs>=1.4.3 in /usr/local/lib/python3.10/dist-packages (from wandb) (1.4.4)\n",
            "Requirement already satisfied: protobuf!=4.21.0,<5,>=3.19.0 in /usr/local/lib/python3.10/dist-packages (from wandb) (3.20.3)\n",
            "Requirement already satisfied: six>=1.4.0 in /usr/local/lib/python3.10/dist-packages (from docker-pycreds>=0.4.0->wandb) (1.16.0)\n",
            "Collecting gitdb<5,>=4.0.1 (from GitPython!=3.1.29,>=1.0.0->wandb)\n",
            "  Downloading gitdb-4.0.11-py3-none-any.whl (62 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (2023.11.17)\n",
            "Collecting smmap<6,>=3.0.1 (from gitdb<5,>=4.0.1->GitPython!=3.1.29,>=1.0.0->wandb)\n",
            "  Downloading smmap-5.0.1-py3-none-any.whl (24 kB)\n",
            "Installing collected packages: smmap, setproctitle, sentry-sdk, docker-pycreds, gitdb, GitPython, wandb\n",
            "Successfully installed GitPython-3.1.40 docker-pycreds-0.4.0 gitdb-4.0.11 sentry-sdk-1.39.0 setproctitle-1.3.3 smmap-5.0.1 wandb-0.16.1\n"
          ]
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#add /coup to path\n",
        "import sys\n",
        "sys.path.append('/content/coup')"
      ],
      "metadata": {
        "id": "y2biuSQqu2ng"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# import statements\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import numpy as np\n",
        "from collections import deque\n",
        "from tqdm.notebook import tqdm\n",
        "import random\n",
        "import matplotlib.pyplot as plt\n",
        "import importlib\n",
        "import wandb\n",
        "\n",
        "import player\n",
        "import rl_player\n",
        "importlib.reload(player)\n",
        "importlib.reload(rl_player)\n",
        "from player import *\n",
        "from rl_player import *\n",
        "\n",
        "#get torch device\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
      ],
      "metadata": {
        "id": "t9jANK-yu0Ed"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "NUM_GAMES = 100\n",
        "\n",
        "def run_simulation(players, shuffle_turn_order=False, num_games=NUM_GAMES):\n",
        "    winners = []\n",
        "\n",
        "    for game_index in range(num_games):\n",
        "        if shuffle_turn_order:\n",
        "            random.shuffle(players)\n",
        "        game = Game(players, debug=False)\n",
        "\n",
        "        turn_count = 0\n",
        "        while len(game.game_state['players']) > 1:\n",
        "            game.simulate_turn()\n",
        "            turn_count += 1\n",
        "            if turn_count > 100:\n",
        "                break\n",
        "        winner = game.game_state['players'][0].name\n",
        "        winners.append(winner)\n",
        "    return winners"
      ],
      "metadata": {
        "id": "E5DfT7kCu626"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train_agent(agent, env, num_episodes, max_steps, batch_size, reward_dict, update_freq, sample_freq, sp_update_freq=10,\n",
        "                win_threshold=0.8, display_progress=False, save_model=False, player_types=[]):\n",
        "    total_rewards = []\n",
        "    win_rates = []\n",
        "    best_winrate = 0.0\n",
        "\n",
        "    for episode in tqdm(range(num_episodes), desc='Episode Loop'):\n",
        "        initial_state = env.reset()\n",
        "        state = initial_state\n",
        "        done = False\n",
        "        total_reward = 0\n",
        "        step = 0\n",
        "\n",
        "        while not done and step <= max_steps:\n",
        "            # Environment stepping\n",
        "            action, next_state, reward, done = env.step(reward_dict)\n",
        "\n",
        "            # For adding experience\n",
        "            agent.add_experience(state, action, reward, next_state, done)\n",
        "\n",
        "            # Update the game state and history for the next iteration\n",
        "            state = next_state\n",
        "            total_reward += reward\n",
        "            step += 1\n",
        "\n",
        "        # Replaying experience\n",
        "        if len(agent.replay_buffer) >= batch_size and step % update_freq == 0:\n",
        "            agent.replay_experience(batch_size, agent.name)\n",
        "\n",
        "        # Decay epsilon\n",
        "        agent.epsilon = max(agent.epsilon * agent.epsilon_decay, agent.epsilon_min)\n",
        "\n",
        "        # Save the total reward for this episode\n",
        "        total_rewards.append(total_reward)\n",
        "\n",
        "        # Log the total reward for this episode\n",
        "        wandb.log({\"total_reward\": total_reward})\n",
        "\n",
        "        if display_progress and episode % sample_freq == 0:\n",
        "            winners = run_simulation(env.players)\n",
        "            win_pct = len([w for w in winners if w == agent.name]) / len(winners)\n",
        "\n",
        "            # Log the win rate for this episode\n",
        "            wandb.log({\"win_rate\": win_pct})\n",
        "            win_rates.append(win_pct)\n",
        "\n",
        "            if win_pct > best_winrate:\n",
        "                # Save the model\n",
        "                best_winrate = win_pct\n",
        "                #agent.save_model('models/{0}-{1}-{2}'.format(len(env.players), num_episodes, ','.join(player_types)))\n",
        "                wandb.save('models/{0}-{1}-{2}'.format(len(env.players), num_episodes, ','.join(player_types)))\n",
        "\n",
        "            # Print win rates\n",
        "            print(f\"Episode {episode + 1}/{num_episodes} - Win Rate: {win_pct} - Epsilon: {agent.epsilon}\")\n",
        "\n",
        "            # If last sp_update_freq win rates are all over threshold, update the other players to main agent's weights\n",
        "            if len(win_rates) >= sp_update_freq and all([w > win_threshold for w in win_rates[-sp_update_freq:]]):\n",
        "                for player in env.players:\n",
        "                    if player.agent is not None and player.name != agent.name:\n",
        "                        # TO-DO: Maybe empty the replay buffer?\n",
        "                        player.agent.model.load_state_dict(agent.model.state_dict())\n",
        "                        player.agent.target_model.load_state_dict(agent.target_model.state_dict())\n",
        "                        print(f\"Updating {player.name} to {agent.name}'s weights\")\n",
        "\n",
        "    if display_progress:\n",
        "        # Plot the win rate over time\n",
        "        plt.plot([x * sample_freq + 1 for x in list(range(len(win_rates)))], win_rates, label=\"agent win rate\")\n",
        "        plt.plot([x * sample_freq + 1 for x in list(range(len(win_rates)))], [1 / len(env.players) for _ in range(len(win_rates))], label=\"expected win rate\", linestyle='dashed')\n",
        "        plt.legend()\n",
        "        plt.title('Q-Learning win rate over time')\n",
        "        plt.ylabel('win rate')\n",
        "        plt.xlabel('number of episodes')\n",
        "        plt.show()\n",
        "\n",
        "    return total_rewards, win_rates"
      ],
      "metadata": {
        "id": "ITj6t7VVu8mA"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def self_play_training(n, reward_dict, learning_rate, gamma, epsilon_decay, h_dim, h_layers, tau,\n",
        "                        buffer_size, num_episodes, batch_size, max_steps_per_episode,\n",
        "                        update_freq, sample_freq, sp_update_freq, win_threshold, history_length=5, display_progress=True):\n",
        "\n",
        "  action_dim = 5\n",
        "  block_size = 2\n",
        "  turn_dim = action_dim + n * 3 + block_size * 2\n",
        "  state_dim = (10 + 11 * n) + (history_length * turn_dim)\n",
        "  action_dim = 1 + 3 * n\n",
        "\n",
        "\n",
        "\n",
        "  agent1 = QLearningAgent(state_dim, action_dim, learning_rate, gamma, 'Player 1', True, history_length=history_length,\n",
        "                          epsilon_decay=epsilon_decay, h_dim= h_dim, h_layers=h_layers, tau=tau, buffer_size=buffer_size)\n",
        "\n",
        "  agents = []\n",
        "  for i in range(2, n+1):\n",
        "    agent = QLearningAgent(state_dim, action_dim, learning_rate, gamma, f'Player {i}', False, history_length=history_length,\n",
        "                          epsilon_decay=epsilon_decay,h_dim= h_dim, h_layers=h_layers, tau=tau, buffer_size=buffer_size)\n",
        "    agents.append(agent)\n",
        "\n",
        "  RLTRAINING_FUNCS = {\n",
        "    'decision_fn': rltraining_decision,\n",
        "    'block_fn': income_block,\n",
        "    'dispose_fn': random_dispose,\n",
        "    'keep_fn': random_keep\n",
        "  }\n",
        "\n",
        "  players = [Player('Player 1', RLTRAINING_FUNCS, agent1)]\n",
        "  for i in range (2, n+1):\n",
        "    players.append(Player(f'Player {i}', RLTRAINING_FUNCS, agents[i-2]))\n",
        "\n",
        "  env = Environment('Player 1', players)\n",
        "\n",
        "\n",
        "  total_rewards, win_rates = train_agent(\n",
        "    agent1, env, num_episodes, max_steps_per_episode, batch_size, reward_dict,\n",
        "    update_freq=update_freq, sample_freq=sample_freq, sp_update_freq=sp_update_freq, win_threshold=win_threshold,\n",
        "    display_progress=display_progress\n",
        "  )\n",
        "\n",
        "  return total_rewards, win_rates\n"
      ],
      "metadata": {
        "id": "AeMhsfQSu-E-"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import wandb\n",
        "wandb.init(project=\"coup-3-player-self-play\")\n",
        "\n",
        "sweep_config = {\n",
        "    'method': 'random',  # Can be \"grid\", \"random\", \"bayes\"\n",
        "    'metric': {\n",
        "      'name': 'win_rate',\n",
        "      'goal': 'maximize'\n",
        "    },\n",
        "    'parameters': {\n",
        "        'learning_rate': {\n",
        "            'min': 1e-4,\n",
        "            'max': 1e-2\n",
        "        },\n",
        "        'update_freq': {\n",
        "            'values': [1, 16, 32, 64, 128]\n",
        "        },\n",
        "        'gamma': {\n",
        "            'values': [0.95, 0.99, 0.999]\n",
        "        },\n",
        "        'epsilon_decay': {\n",
        "            'min': 0.99,\n",
        "            'max': 0.9999\n",
        "        },\n",
        "        'h_dim': {\n",
        "            'values': [128, 256, 512, 1024]\n",
        "        },\n",
        "        'h_layers': {\n",
        "            'values': [2, 3, 4, 5, 6]\n",
        "        },\n",
        "        'tau': {\n",
        "            'min': 1e-4,\n",
        "            'max': 1e-2\n",
        "        },\n",
        "        'batch_size': {\n",
        "            'values': [32, 64, 128, 1024, 4096]\n",
        "        },\n",
        "        'history_length': {\n",
        "            'values': [10, 20, 30, 40, 50]\n",
        "        },\n",
        "        'buffer_size': {\n",
        "            'values': [1e4, 1e5, 1e6, 1e7, 1e8]\n",
        "        },\n",
        "\n",
        "        'COIN_VALUE': {\n",
        "            'min': 0.1,\n",
        "            'max': 0.7\n",
        "        },\n",
        "        'CARD_VALUE': {\n",
        "            'min': 0.3,\n",
        "            'max': 0.7\n",
        "        },\n",
        "        'CARD_DIVERSITY_VALUE': {\n",
        "            'min': 0.0,\n",
        "            'max': 0.3\n",
        "        },\n",
        "        'WIN_VALUE': {\n",
        "            'min': 0.5,\n",
        "            'max': 1.0\n",
        "        },\n",
        "    }\n",
        "}"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "id": "00VrtXmOhySs",
        "outputId": "e58e80a4-ea1a-4223-a629-31e6f9d92d60"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mokg21\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Tracking run with wandb version 0.16.1"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20231212_195938-u0ti51m7</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/okg21/coup-3-player-self-play/runs/u0ti51m7' target=\"_blank\">blooming-valley-1</a></strong> to <a href='https://wandb.ai/okg21/coup-3-player-self-play' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View project at <a href='https://wandb.ai/okg21/coup-3-player-self-play' target=\"_blank\">https://wandb.ai/okg21/coup-3-player-self-play</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run at <a href='https://wandb.ai/okg21/coup-3-player-self-play/runs/u0ti51m7' target=\"_blank\">https://wandb.ai/okg21/coup-3-player-self-play/runs/u0ti51m7</a>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def train():\n",
        "    # Initialize a new wandb run\n",
        "    with wandb.init() as run:\n",
        "        config = wandb.config\n",
        "\n",
        "        reward_dict = {\n",
        "            'COIN_VALUE': config.COIN_VALUE,\n",
        "            'CARD_VALUE': config.CARD_VALUE,\n",
        "            'CARD_DIVERSITY_VALUE': config.CARD_DIVERSITY_VALUE,\n",
        "            'WIN_VALUE': config.WIN_VALUE\n",
        "        }\n",
        "\n",
        "        # Call your training function with wandb's config\n",
        "        total_rewards, win_rates = self_play_training(\n",
        "            n=3,\n",
        "            reward_dict=reward_dict,\n",
        "            learning_rate=config.learning_rate,\n",
        "            gamma=config.gamma,\n",
        "            epsilon_decay=config.epsilon_decay,\n",
        "            h_dim=config.h_dim,\n",
        "            h_layers=config.h_layers,\n",
        "            tau=config.tau,\n",
        "            buffer_size=config.buffer_size,\n",
        "            num_episodes=1000,\n",
        "            batch_size=config.batch_size,\n",
        "            max_steps_per_episode=100,\n",
        "            update_freq=config.update_freq,\n",
        "            sp_update_freq=5,\n",
        "            sample_freq=100,\n",
        "            win_threshold=0.9,\n",
        "            history_length=config.history_length,\n",
        "            display_progress=True\n",
        "        )\n",
        "\n",
        "        # Log maximum and average values of total_rewards\n",
        "        wandb.log({\"max_total_reward\": max(total_rewards)})\n",
        "        wandb.log({\"avg_total_reward\": sum(total_rewards) / len(total_rewards)})\n",
        "\n",
        "        # Log maximum and average values of win_rates\n",
        "        wandb.log({\"max_win_rate\": max(win_rates)})\n",
        "        wandb.log({\"avg_win_rate\": sum(win_rates) / len(win_rates)})\n",
        "\n"
      ],
      "metadata": {
        "id": "D8N6pAfHbKR7"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sweep_id = wandb.sweep(sweep_config, project=\"coup-3-player-self-play\")\n",
        "wandb.agent(sweep_id, train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "1a09aa1bc070414ba4b1949df7734cf1",
            "dfa9970a936846e78fddf6443908f885",
            "5949c4ec579f435eb99b4d9502e44463",
            "8014d8650fd148a98a10422a9d615079",
            "3097064d95eb4830afd35bb5dc9f84e2",
            "b81a5143ae8444cd9f41e447e02ffe74",
            "605f45ea5a634dc8984e006d3e286fb1",
            "bf9521a3a3354f028adccec094a46108",
            "72fbb0455e884448858f45743997436c",
            "4012e032f0434d7a80a19ff48251e564",
            "8f598278a8644079a21f184abb33a73d",
            "d10def600c414d688fde8fe230c11940",
            "f0f8832c318d45bdba2053404b9727ba",
            "5ffe986de095406a9c4fc63818224f4e",
            "785939da03094a96973f6d7c9a9cfed3",
            "a6a04769016b4ccd817c70a3afe053e5",
            "bf9e4becb43f4131aa5b36b09be5a55a",
            "b2f6ed5cec884ebb8f0152a090f022a7",
            "bd9a03274d014ede8a18617c94c9191e",
            "c1de641001994ff0a03339c90137f883",
            "e2aeb1e202184cf297fd4e50add4bd78",
            "681e01ebd69c435c9ab0a5f170d4a797",
            "3006a23899e2462f99d2607bfba75ba2",
            "10b630a1ec664f609c1af0b926f112a1",
            "b012b99094d0490abab51f89a7ba2b10",
            "b0f60fea17684475969671189b4c1881",
            "ad55425ea75d46bbab26c790ae9cd236",
            "718311190d1248fd912204fa9bf82dc4",
            "85b73703db1d43349f0d0effe034ac18",
            "bbac6a632cf24852acb381bac0b1c2c4"
          ]
        },
        "id": "a_zmeW-UkhGZ",
        "outputId": "ef6a02f6-6a7b-47c4-b3fa-6817c90ba2c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Calling wandb.login() after wandb.init() has no effect.\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Create sweep with ID: 1ijcsiwv\n",
            "Sweep URL: https://wandb.ai/okg21/coup-3-player-self-play/sweeps/1ijcsiwv\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Exception in thread IntMsgThr:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/lib/python3.10/threading.py\", line 1016, in _bootstrap_inner\n",
            "    self.run()\n",
            "  File \"/usr/lib/python3.10/threading.py\", line 953, in run\n",
            "    self._target(*self._args, **self._kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/wandb/sdk/wandb_run.py\", line 300, in check_internal_messages\n",
            "    self._loop_check_status(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/wandb/sdk/wandb_run.py\", line 224, in _loop_check_status\n",
            "    local_handle = request()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/wandb/sdk/interface/interface.py\", line 764, in deliver_internal_messages\n",
            "    return self._deliver_internal_messages(internal_message)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/wandb/sdk/interface/interface_shared.py\", line 490, in _deliver_internal_messages\n",
            "    return self._deliver_record(record)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/wandb/sdk/interface/interface_shared.py\", line 437, in _deliver_record\n",
            "    handle = mailbox._deliver_record(record, interface=self)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/wandb/sdk/lib/mailbox.py\", line 455, in _deliver_record\n",
            "    interface._publish(record)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/wandb/sdk/interface/interface_sock.py\", line 51, in _publish\n",
            "    self._sock_client.send_record_publish(record)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/wandb/sdk/lib/sock_client.py\", line 221, in send_record_publish\n",
            "    self.send_server_request(server_req)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/wandb/sdk/lib/sock_client.py\", line 155, in send_server_request\n",
            "    self._send_message(msg)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/wandb/sdk/lib/sock_client.py\", line 152, in _send_message\n",
            "    self._sendall_with_error_handle(header + data)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/wandb/sdk/lib/sock_client.py\", line 130, in _sendall_with_error_handle\n",
            "    sent = self._sock.send(data)\n",
            "BrokenPipeError: [Errno 32] Broken pipe\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: gkdxrch0 with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tCARD_DIVERSITY_VALUE: 0.2970453962319831\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tCARD_VALUE: 0.5535699149701169\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tCOIN_VALUE: 0.618967667374559\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tWIN_VALUE: 0.5721025483896036\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 128\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tbuffer_size: 1000000\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tepsilon_decay: 0.9956156557239448\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.99\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \th_dim: 256\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \th_layers: 2\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \thistory_length: 50\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.0007931331176155576\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \ttau: 0.005347844929974799\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tupdate_freq: 128\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "Tracking run with wandb version 0.16.1"
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              "<IPython.core.display.HTML object>"
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              "Run data is saved locally in <code>/content/wandb/run-20231212_200000-gkdxrch0</code>"
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        {
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            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/okg21/coup-3-player-self-play/runs/gkdxrch0' target=\"_blank\">silvery-sweep-1</a></strong> to <a href='https://wandb.ai/okg21/coup-3-player-self-play' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/okg21/coup-3-player-self-play/sweeps/1ijcsiwv' target=\"_blank\">https://wandb.ai/okg21/coup-3-player-self-play/sweeps/1ijcsiwv</a>"
            ],
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              "<IPython.core.display.HTML object>"
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          },
          "metadata": {},
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        },
        {
          "data": {
            "text/html": [
              " View project at <a href='https://wandb.ai/okg21/coup-3-player-self-play' target=\"_blank\">https://wandb.ai/okg21/coup-3-player-self-play</a>"
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              "<IPython.core.display.HTML object>"
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        {
          "data": {
            "text/html": [
              " View sweep at <a href='https://wandb.ai/okg21/coup-3-player-self-play/sweeps/1ijcsiwv' target=\"_blank\">https://wandb.ai/okg21/coup-3-player-self-play/sweeps/1ijcsiwv</a>"
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              "<IPython.core.display.HTML object>"
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          "metadata": {},
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        },
        {
          "data": {
            "text/html": [
              " View run at <a href='https://wandb.ai/okg21/coup-3-player-self-play/runs/gkdxrch0' target=\"_blank\">https://wandb.ai/okg21/coup-3-player-self-play/runs/gkdxrch0</a>"
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1a09aa1bc070414ba4b1949df7734cf1",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Episode Loop:   0%|          | 0/1000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Episode 1/1000 - Win Rate: 0.35 - Epsilon: 0.9956156557239448\n",
            "Episode 101/1000 - Win Rate: 0.23 - Epsilon: 0.6415986326581747\n",
            "Episode 201/1000 - Win Rate: 0.16 - Epsilon: 0.41346156326711825\n",
            "Episode 301/1000 - Win Rate: 0.16 - Epsilon: 0.2664445583230644\n",
            "Episode 401/1000 - Win Rate: 0.22 - Epsilon: 0.1717032705507083\n",
            "Episode 501/1000 - Win Rate: 0.16 - Epsilon: 0.11064971003109304\n",
            "Episode 601/1000 - Win Rate: 0.23 - Epsilon: 0.07130532977442151\n",
            "Episode 701/1000 - Win Rate: 0.21 - Epsilon: 0.04595086650304143\n",
            "Episode 801/1000 - Win Rate: 0.19 - Epsilon: 0.02961184162614674\n",
            "Episode 901/1000 - Win Rate: 0.25 - Epsilon: 0.019082581705699913\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(Label(value='0.001 MB of 0.001 MB uploaded\\r'), FloatProgress(value=1.0, max=1.0)))"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d10def600c414d688fde8fe230c11940"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
              "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
              "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>avg_total_reward</td><td>▁</td></tr><tr><td>avg_win_rate</td><td>▁</td></tr><tr><td>max_total_reward</td><td>▁</td></tr><tr><td>max_win_rate</td><td>▁</td></tr><tr><td>total_reward</td><td>▂▅▅▆▅▅▂▂▂▂▂▂▂▂▇▂▂▂▆█▁▂▁▁▁▁▁▁██▁█▁▁▁▁▁▁▁█</td></tr><tr><td>win_rate</td><td>█▄▁▁▃▁▄▃▂▄</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>avg_total_reward</td><td>0.57911</td></tr><tr><td>avg_win_rate</td><td>0.216</td></tr><tr><td>max_total_reward</td><td>17.52843</td></tr><tr><td>max_win_rate</td><td>0.35</td></tr><tr><td>total_reward</td><td>12.65476</td></tr><tr><td>win_rate</td><td>0.25</td></tr></table><br/></div></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run <strong style=\"color:#cdcd00\">silvery-sweep-1</strong> at: <a href='https://wandb.ai/okg21/coup-3-player-self-play/runs/gkdxrch0' target=\"_blank\">https://wandb.ai/okg21/coup-3-player-self-play/runs/gkdxrch0</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Find logs at: <code>./wandb/run-20231212_200000-gkdxrch0/logs</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: y2hzi3ou with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tCARD_DIVERSITY_VALUE: 0.10269884105615736\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tCARD_VALUE: 0.465062760453368\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tCOIN_VALUE: 0.6814490028342359\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tWIN_VALUE: 0.7643801853481313\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 128\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tbuffer_size: 10000\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tepsilon_decay: 0.9985812504192332\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tgamma: 0.99\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \th_dim: 512\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \th_layers: 4\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \thistory_length: 20\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.0022887511781270547\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \ttau: 0.0014617779452071608\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tupdate_freq: 1\n"
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              "Tracking run with wandb version 0.16.1"
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              "<IPython.core.display.HTML object>"
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              "Run data is saved locally in <code>/content/wandb/run-20231212_200246-y2hzi3ou</code>"
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              "<IPython.core.display.HTML object>"
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            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/okg21/coup-3-player-self-play/runs/y2hzi3ou' target=\"_blank\">devout-sweep-2</a></strong> to <a href='https://wandb.ai/okg21/coup-3-player-self-play' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/okg21/coup-3-player-self-play/sweeps/1ijcsiwv' target=\"_blank\">https://wandb.ai/okg21/coup-3-player-self-play/sweeps/1ijcsiwv</a>"
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              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View project at <a href='https://wandb.ai/okg21/coup-3-player-self-play' target=\"_blank\">https://wandb.ai/okg21/coup-3-player-self-play</a>"
            ]
          },
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        {
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          "data": {
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              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View sweep at <a href='https://wandb.ai/okg21/coup-3-player-self-play/sweeps/1ijcsiwv' target=\"_blank\">https://wandb.ai/okg21/coup-3-player-self-play/sweeps/1ijcsiwv</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run at <a href='https://wandb.ai/okg21/coup-3-player-self-play/runs/y2hzi3ou' target=\"_blank\">https://wandb.ai/okg21/coup-3-player-self-play/runs/y2hzi3ou</a>"
            ]
          },
          "metadata": {}
        },
        {
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          "data": {
            "text/plain": [
              "Episode Loop:   0%|          | 0/1000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c1de641001994ff0a03339c90137f883"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Episode 1/1000 - Win Rate: 0.66 - Epsilon: 0.9985812504192332\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "u_IQAkESkyNy"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}