20240211_Navigation_DQN_train.ipynb

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        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Nov05/Google-Colaboratory/blob/master/20240211_Navigation_DQN_train.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **\\<TOP>**\n",
        "\n",
        "* modified by nov05 on 2024-02-09  \n",
        "* check the original notebook on [GitHub](https://github.com/udacity/deep-reinforcement-learning/tree/master/p1_navigation)  \n",
        "  the dependency folder `./python` on [GitHub](https://github.com/udacity/deep-reinforcement-learning/tree/master/python)       \n",
        "* refer to [the DQN notebook](https://drive.google.com/file/d/1J4tO1sFULVQ5MAhRfTz4gpThqd3_NgZR)  \n",
        "* go to [the eval notebook](https://github.com/Nov05/udacity-deep-reinforcement-learning/blob/master/p1_navigation/20240211_Navigation_DQN_eval.ipynb) (run in Windows 11)\n",
        "   "
      ],
      "metadata": {
        "id": "YoyHLk7nCbpl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!cat /etc/*release\n",
        "## Ubuntu 22.04.3 LTS (Jammy Jellyfish)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w3XfoZ4aGEiB",
        "outputId": "dfa0215c-2592-4c2e-d943-0dc504485748"
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      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DISTRIB_ID=Ubuntu\n",
            "DISTRIB_RELEASE=22.04\n",
            "DISTRIB_CODENAME=jammy\n",
            "DISTRIB_DESCRIPTION=\"Ubuntu 22.04.3 LTS\"\n",
            "PRETTY_NAME=\"Ubuntu 22.04.3 LTS\"\n",
            "NAME=\"Ubuntu\"\n",
            "VERSION_ID=\"22.04\"\n",
            "VERSION=\"22.04.3 LTS (Jammy Jellyfish)\"\n",
            "VERSION_CODENAME=jammy\n",
            "ID=ubuntu\n",
            "ID_LIKE=debian\n",
            "HOME_URL=\"https://www.ubuntu.com/\"\n",
            "SUPPORT_URL=\"https://help.ubuntu.com/\"\n",
            "BUG_REPORT_URL=\"https://bugs.launchpad.net/ubuntu/\"\n",
            "PRIVACY_POLICY_URL=\"https://www.ubuntu.com/legal/terms-and-policies/privacy-policy\"\n",
            "UBUNTU_CODENAME=jammy\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "## download the \"\"./python\" folder.\n",
        "## https://github.com/udacity/deep-reinforcement-learning/tree/master/python\n",
        "import gdown\n",
        "url = \"https://drive.google.com/drive/folders/10a5WA1afaEIRgfYz7513kEc0eJTQQ1mK\"\n",
        "gdown.download_folder(url)"
      ],
      "metadata": {
        "id": "udH5oFm8eTeE"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## change working directory\n",
        "%cd python\n",
        "## get the banana build. you don't need to install ml-agents.\n",
        "!wget https://s3-us-west-1.amazonaws.com/udacity-drlnd/P1/Banana/Banana_Linux.zip\n",
        "!unzip Banana_Linux.zip\n",
        "!rm Banana_Linux.zip"
      ],
      "metadata": {
        "id": "AfIssFbUDF7K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6910ec6d-9d83-4d80-8c84-97bc38194c1b"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/python\n",
            "--2024-02-12 05:17:16--  https://s3-us-west-1.amazonaws.com/udacity-drlnd/P1/Banana/Banana_Linux.zip\n",
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            "Connecting to s3-us-west-1.amazonaws.com (s3-us-west-1.amazonaws.com)|52.219.216.0|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
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            "Saving to: ‘Banana_Linux.zip’\n",
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            "\n",
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      "cell_type": "markdown",
      "metadata": {
        "id": "ebUQoIf5Cabc"
      },
      "source": [
        "# **Navigation**   \n",
        "\n",
        "---\n",
        "\n",
        "In this notebook, you will learn how to use the Unity ML-Agents environment for the first project of the [Deep Reinforcement Learning Nanodegree](https://www.udacity.com/course/deep-reinforcement-learning-nanodegree--nd893)."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **1. Start the Environment**  \n",
        "\n",
        "We begin by importing some necessary packages.  If the code cell below returns an error, please revisit the project instructions to double-check that you have installed [Unity ML-Agents](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Installation.md) and [NumPy](http://www.numpy.org/)."
      ],
      "metadata": {
        "id": "CunpqWwGUsVe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "👉 If you restart the session (Ctrl+M.), run from the following cell.  "
      ],
      "metadata": {
        "id": "saPrh1GlyO3b"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "oFKuzS6ECabg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "62bbfd8a-4470-4ac5-84ba-fd03c66d908b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/python\n"
          ]
        }
      ],
      "source": [
        "%cd python\n",
        "from unityagents import UnityEnvironment\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nSUan7BLCabh"
      },
      "source": [
        "Next, we will start the environment!  **_Before running the code cell below_**, change the `file_name` parameter to match the location of the Unity environment that you downloaded.\n",
        "\n",
        "- **Mac**: `\"path/to/Banana.app\"`\n",
        "- **Windows** (x86): `\"path/to/Banana_Windows_x86/Banana.exe\"`\n",
        "- **Windows** (x86_64): `\"path/to/Banana_Windows_x86_64/Banana.exe\"`\n",
        "- **Linux** (x86): `\"path/to/Banana_Linux/Banana.x86\"`\n",
        "- **Linux** (x86_64): `\"path/to/Banana_Linux/Banana.x86_64\"`\n",
        "- **Linux** (x86, headless): `\"path/to/Banana_Linux_NoVis/Banana.x86\"`\n",
        "- **Linux** (x86_64, headless): `\"path/to/Banana_Linux_NoVis/Banana.x86_64\"`\n",
        "\n",
        "For instance, if you are using a Mac, then you downloaded `Banana.app`.  If this file is in the same folder as the notebook, then the line below should appear as follows:\n",
        "```\n",
        "env = UnityEnvironment(file_name=\"Banana.app\")\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CyHlsgF3Cabh",
        "outputId": "1770be37-96c4-4df1-a7b1-98a978b1cacc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 12 ms, sys: 2.9 ms, total: 14.9 ms\n",
            "Wall time: 1.55 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "env = UnityEnvironment(file_name=\"./Banana_Linux/Banana.x86_64\", no_graphics=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qJ7AKsSjCabi"
      },
      "source": [
        "Environments contain **_brains_** which are responsible for deciding the actions of their associated agents. Here we check for the first brain available, and set it as the default brain we will be controlling from Python."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "37OjyA8kCabi",
        "outputId": "9d93e8ca-44c1-450e-c90c-bb51732bb25e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unity brain name: BananaBrain\n",
            "        Number of Visual Observations (per agent): 0\n",
            "        Vector Observation space type: continuous\n",
            "        Vector Observation space size (per agent): 37\n",
            "        Number of stacked Vector Observation: 1\n",
            "        Vector Action space type: discrete\n",
            "        Vector Action space size (per agent): 4\n",
            "        Vector Action descriptions: , , , \n"
          ]
        }
      ],
      "source": [
        "# get the default brain\n",
        "brain_name = env.brain_names[0]\n",
        "brain = env.brains[brain_name]\n",
        "print(brain)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VRatfghmCabi"
      },
      "source": [
        "### **2. Examine the State and Action Spaces**   \n",
        "\n",
        "The simulation contains a single agent that navigates a large environment.  At each time step, it has four actions at its disposal:\n",
        "- `0` - walk forward\n",
        "- `1` - walk backward\n",
        "- `2` - turn left\n",
        "- `3` - turn right\n",
        "\n",
        "The state space has `37` dimensions and contains the agent's velocity, along with ray-based perception of objects around agent's forward direction.  A reward of `+1` is provided for collecting a yellow banana, and a reward of `-1` is provided for collecting a blue banana.\n",
        "\n",
        "Run the code cell below to print some information about the environment."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1iWX9CxqCabi",
        "outputId": "4949ed70-42fd-4c27-98f4-5f9432c8a58b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of agents: 1\n",
            "Number of actions: 4\n",
            "States look like:\n",
            " [1.         0.         0.         0.         0.84408134 0.\n",
            " 0.         1.         0.         0.0748472  0.         1.\n",
            " 0.         0.         0.25755    1.         0.         0.\n",
            " 0.         0.74177343 0.         1.         0.         0.\n",
            " 0.25854847 0.         0.         1.         0.         0.09355672\n",
            " 0.         1.         0.         0.         0.31969345 0.\n",
            " 0.        ]\n",
            "States have length: 37\n"
          ]
        }
      ],
      "source": [
        "## reset the environment\n",
        "env_info = env.reset(train_mode=True)[brain_name]\n",
        "## number of agents in the environment\n",
        "print('Number of agents:', len(env_info.agents))\n",
        "## number of actions\n",
        "action_size = brain.vector_action_space_size\n",
        "print('Number of actions:', action_size)\n",
        "## examine the state space\n",
        "state = env_info.vector_observations[0]\n",
        "print('States look like:\\n', state)\n",
        "state_size = len(state)\n",
        "print('States have length:', state_size)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N1uk5TwTCabj"
      },
      "source": [
        "### **3. Take Random Actions in the Environment**  \n",
        "\n",
        "In the next code cell, you will learn how to use the Python API to control the agent and receive feedback from the environment.\n",
        "\n",
        "Once this cell is executed, you will watch the agent's performance, if it selects an action (uniformly) at random with each time step.  A window should pop up that allows you to observe the agent, as it moves through the environment.  \n",
        "\n",
        "Of course, as part of the project, you'll have to change the code so that the agent is able to use its experience to gradually choose better actions when interacting with the environment!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "Q4fkinLMCabj"
      },
      "outputs": [],
      "source": [
        "# %%time\n",
        "# env_info = env.reset(train_mode=False)[brain_name] # reset the environment\n",
        "# state = env_info.vector_observations[0]            # get the current state\n",
        "# score = 0                                          # initialize the score\n",
        "# while True:\n",
        "#     action = np.random.randint(action_size)        # select an action\n",
        "#     env_info = env.step(action)[brain_name]        # send the action to the environment\n",
        "#     next_state = env_info.vector_observations[0]   # get the next state\n",
        "#     reward = env_info.rewards[0]                   # get the reward\n",
        "#     done = env_info.local_done[0]                  # see if episode has finished\n",
        "#     score += reward                                # update the score\n",
        "#     state = next_state                             # roll over the state to next time step\n",
        "#     if done:                                       # exit loop if episode finished\n",
        "#         break\n",
        "# print(\"Score: {}\".format(score))\n",
        "# # env.close()\n",
        "# ## Wall time: 29.8 s"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ujhLsquRCabk"
      },
      "source": [
        "When finished, you can close the environment."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zi47q_qRCabk"
      },
      "source": [
        "### **4. It's Your Turn!**  \n",
        "\n",
        "Now it's your turn to train your own agent to solve the environment!  When training the environment, set `train_mode=True`, so that the line for resetting the environment looks like the following:\n",
        "```python\n",
        "env_info = env.reset(train_mode=True)[brain_name]\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "class QNetwork(nn.Module):\n",
        "    \"\"\"Actor (Policy) Model.\"\"\"\n",
        "\n",
        "    def __init__(self, state_size, action_size, seed):\n",
        "        \"\"\"Initialize parameters and build model.\n",
        "        Params\n",
        "        ======\n",
        "            state_size (int): Dimension of each state\n",
        "            action_size (int): Dimension of each action\n",
        "            seed (int): Random seed\n",
        "        \"\"\"\n",
        "        super(QNetwork, self).__init__()\n",
        "        self.seed = torch.manual_seed(seed)\n",
        "        \"*** YOUR CODE HERE ***\"\n",
        "        self.layer1 = nn.Linear(state_size, 256)\n",
        "        self.layer2 = nn.Linear(256, 64)\n",
        "        self.layer3 = nn.Linear(64, action_size)\n",
        "\n",
        "    def forward(self, state):\n",
        "        \"\"\"Build a network that maps state -> action values.\"\"\"\n",
        "        x = F.relu(self.layer1(state))\n",
        "        x = F.relu(self.layer2(x))\n",
        "        return self.layer3(x)"
      ],
      "metadata": {
        "id": "heidiYbOcioo"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import random\n",
        "from collections import namedtuple, deque\n",
        "\n",
        "# from model import QNetwork ## local import\n",
        "\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "\n",
        "BUFFER_SIZE = int(1e5)  # replay buffer size\n",
        "BATCH_SIZE = 64         # minibatch size\n",
        "GAMMA = 0.99            # discount factor\n",
        "TAU = 1e-3              # for soft update of target parameters\n",
        "LR = 5e-4               # learning rate\n",
        "UPDATE_EVERY = 4        # how often to update the network\n",
        "\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "class ReplayBuffer:\n",
        "    \"\"\"Fixed-size buffer to store experience tuples.\"\"\"\n",
        "\n",
        "    def __init__(self, action_size, buffer_size, batch_size, seed):\n",
        "        \"\"\"Initialize a ReplayBuffer object.\n",
        "\n",
        "        Params\n",
        "        ======\n",
        "            action_size (int): dimension of each action\n",
        "            buffer_size (int): maximum size of buffer\n",
        "            batch_size (int): size of each training batch\n",
        "            seed (int): random seed\n",
        "        \"\"\"\n",
        "        self.action_size = action_size\n",
        "        self.memory = deque(maxlen=buffer_size)\n",
        "        self.batch_size = batch_size\n",
        "        self.experience = namedtuple(\"Experience\", field_names=[\"state\", \"action\", \"reward\", \"next_state\", \"done\"])\n",
        "        self.seed = random.seed(seed)\n",
        "\n",
        "    def add(self, state, action, reward, next_state, done):\n",
        "        \"\"\"Add a new experience to memory.\"\"\"\n",
        "        e = self.experience(state, action, reward, next_state, done)\n",
        "        self.memory.append(e)\n",
        "\n",
        "    def sample(self):\n",
        "        \"\"\"Randomly sample a batch of experiences from memory.\"\"\"\n",
        "        experiences = random.sample(self.memory, k=self.batch_size)\n",
        "\n",
        "        states = torch.from_numpy(np.vstack([e.state for e in experiences if e is not None])).float().to(device)\n",
        "        actions = torch.from_numpy(np.vstack([e.action for e in experiences if e is not None])).long().to(device)\n",
        "        rewards = torch.from_numpy(np.vstack([e.reward for e in experiences if e is not None])).float().to(device)\n",
        "        next_states = torch.from_numpy(np.vstack([e.next_state for e in experiences if e is not None])).float().to(device)\n",
        "        dones = torch.from_numpy(np.vstack([e.done for e in experiences if e is not None]).astype(np.uint8)).float().to(device)\n",
        "\n",
        "        return (states, actions, rewards, next_states, dones)\n",
        "\n",
        "    def __len__(self):\n",
        "        \"\"\"Return the current size of internal memory.\"\"\"\n",
        "        return len(self.memory)"
      ],
      "metadata": {
        "id": "xSDkHEfQc8Yr"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Agent():\n",
        "    \"\"\"Interacts with and learns from the environment.\"\"\"\n",
        "\n",
        "    def __init__(self, state_size, action_size, seed):\n",
        "        \"\"\"Initialize an Agent object.\n",
        "\n",
        "        Params\n",
        "        ======\n",
        "            state_size (int): dimension of each state\n",
        "            action_size (int): dimension of each action\n",
        "            seed (int): random seed\n",
        "        \"\"\"\n",
        "        self.state_size = state_size\n",
        "        self.action_size = action_size\n",
        "        self.seed = random.seed(seed)\n",
        "\n",
        "        # Q-Network\n",
        "        self.qnetwork_local = QNetwork(state_size, action_size, seed).to(device)\n",
        "        self.qnetwork_target = QNetwork(state_size, action_size, seed).to(device)\n",
        "        self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)\n",
        "\n",
        "        # Replay memory\n",
        "        self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)\n",
        "        # Initialize time step (for updating every UPDATE_EVERY steps)\n",
        "        self.t_step = 0\n",
        "\n",
        "    def step(self, state, action, reward, next_state, done):\n",
        "        # Save experience in replay memory\n",
        "        self.memory.add(state, action, reward, next_state, done)\n",
        "\n",
        "        # Learn every UPDATE_EVERY time steps.\n",
        "        self.t_step = (self.t_step + 1) % UPDATE_EVERY\n",
        "        if self.t_step == 0:\n",
        "            # If enough samples are available in memory, get random subset and learn\n",
        "            if len(self.memory) > BATCH_SIZE:\n",
        "                experiences = self.memory.sample()\n",
        "                self.learn(experiences, GAMMA)\n",
        "\n",
        "    def act(self, state, eps=0.):\n",
        "        \"\"\"Returns actions for given state as per current policy.\n",
        "\n",
        "        Params\n",
        "        ======\n",
        "            state (array_like): current state\n",
        "            eps (float): epsilon, for epsilon-greedy action selection\n",
        "        \"\"\"\n",
        "        state = torch.from_numpy(state).float().unsqueeze(0).to(device)\n",
        "        self.qnetwork_local.eval()\n",
        "        with torch.no_grad():\n",
        "            action_values = self.qnetwork_local(state)\n",
        "        self.qnetwork_local.train()\n",
        "\n",
        "        # Epsilon-greedy action selection\n",
        "        if random.random() > eps:\n",
        "            return np.argmax(action_values.cpu().data.numpy())\n",
        "        else:\n",
        "            return random.choice(np.arange(self.action_size))\n",
        "\n",
        "    def learn(self, experiences, gamma):\n",
        "        \"\"\"Update value parameters using given batch of experience tuples.\n",
        "        target_j = reward_j + gamma * max_a ()\n",
        "\n",
        "        Params\n",
        "        ======\n",
        "            experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples\n",
        "            gamma (float): discount factor\n",
        "        \"\"\"\n",
        "        states, actions, rewards, next_states, dones = experiences\n",
        "\n",
        "        ## TODO: compute and minimize the loss\n",
        "        \"*** YOUR CODE HERE ***\"\n",
        "        # Get max predicted Q values (for next states) from target model\n",
        "        Q_targets_next = self.qnetwork_target(next_states).detach().max(1)[0].unsqueeze(1)\n",
        "        # Compute Q targets for current states\n",
        "        Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))\n",
        "\n",
        "        # Get expected Q values from local model\n",
        "        Q_expected = self.qnetwork_local(states).gather(1, actions)\n",
        "\n",
        "        # Compute loss\n",
        "        loss = F.mse_loss(Q_expected, Q_targets)\n",
        "        # Minimize the loss\n",
        "        self.optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        self.optimizer.step()\n",
        "\n",
        "        # ------------------- update target network ------------------- #\n",
        "        self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU)\n",
        "\n",
        "    def soft_update(self, local_model, target_model, tau):\n",
        "        \"\"\"Soft update model parameters.\n",
        "        θ_target = τ*θ_local + (1 - τ)*θ_target\n",
        "\n",
        "        Params\n",
        "        ======\n",
        "            local_model (PyTorch model): weights will be copied from\n",
        "            target_model (PyTorch model): weights will be copied to\n",
        "            tau (float): interpolation parameter\n",
        "        \"\"\"\n",
        "        for target_param, local_param in zip(target_model.parameters(), local_model.parameters()):\n",
        "            target_param.data.copy_(tau*local_param.data + (1.0-tau)*target_param.data)"
      ],
      "metadata": {
        "id": "jkWEhpWodak6"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "agent = Agent(state_size=state_size, action_size=action_size, seed=0)\n",
        "# print(agent.qnetwork_local)\n",
        "for param in list(agent.qnetwork_local.parameters()):\n",
        "    print(param.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XrL6hmKSdkXw",
        "outputId": "dce66ad5-8925-4de1-aa32-41914dc367f3"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([256, 37])\n",
            "torch.Size([256])\n",
            "torch.Size([64, 256])\n",
            "torch.Size([64])\n",
            "torch.Size([4, 64])\n",
            "torch.Size([4])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Benchmark Implementation**  \n",
        "\n",
        "In case you get stuck, note that you should be able to solve the project by making only minor modifications to the DQN code provided as part of the Deep Q-Networks lesson. Please see the image below for an example of how you might expect your agent's score to evolve. If you're interested in about how long it should take, in the solution code for the project, we were able to solve the project **in fewer than 1800 episodes**."
      ],
      "metadata": {
        "id": "VDq9ciUTo6qt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "import sys\n",
        "from tqdm import tqdm\n",
        "\n",
        "def dqn(n_episodes=2000, max_t=1000, eps_start=1.0, eps_end=0.01, eps_decay=0.995):\n",
        "    \"\"\"Deep Q-Learning.\n",
        "\n",
        "    Params\n",
        "    ======\n",
        "        n_episodes (int): maximum number of training episodes\n",
        "        max_t (int): maximum number of timesteps per episode\n",
        "        eps_start (float): starting value of epsilon, for epsilon-greedy action selection\n",
        "        eps_end (float): minimum value of epsilon\n",
        "        eps_decay (float): multiplicative factor (per episode) for decreasing epsilon\n",
        "    \"\"\"\n",
        "    scores = []                        # list containing scores from each episode\n",
        "    scores_window = deque(maxlen=100)  # last 100 scores\n",
        "    score_max = float('-inf')\n",
        "    is_solved = False\n",
        "    eps = eps_start                    # initialize epsilon\n",
        "    for i_episode in range(1, n_episodes+1):\n",
        "    # for i_episode in tqdm(range(1, n_episodes+1), desc=\"Episodes\", position=0, leave=True):\n",
        "        state = env.reset(train_mode=True)[brain_name].vector_observations[0]\n",
        "        score = 0\n",
        "        for t in range(max_t):\n",
        "            action = agent.act(state, eps)\n",
        "            env_info = env.step(action)[brain_name]\n",
        "            next_state, reward, done = env_info.vector_observations[0], \\\n",
        "                                       env_info.rewards[0], env_info.local_done[0]\n",
        "            agent.step(state, action, reward, next_state, done) ## learn every UPDATE_EVERY = 4 steps\n",
        "            state = next_state\n",
        "            score += reward\n",
        "            if done:\n",
        "                break\n",
        "        scores_window.append(score)       # save most recent score\n",
        "        scores.append(score)              # save most recent score\n",
        "        eps = max(eps_end, eps_decay*eps) # decrease epsilon\n",
        "        print('\\rEpisode {}\\tAverage Score: {:.2f}'.format(i_episode, np.mean(scores_window)), end=\"\")\n",
        "        if i_episode % 100==0:\n",
        "            print('\\rEpisode {}\\tAverage Score: {:.2f}'.format(i_episode, np.mean(scores_window)))\n",
        "        scores_mean = np.mean(scores_window)\n",
        "        if scores_mean>=20. and scores_mean>score_max:\n",
        "            score_max = scores_mean\n",
        "            if not is_solved:\n",
        "                is_solved = True\n",
        "                print('\\nEnvironment solved in {:d} episodes!'.format(i_episode-100))\n",
        "            print(\"\\nCheckpoint is saved at episode {:d}, average score: {:.2f}\".format(i_episode, np.mean(scores_window)))\n",
        "            torch.save(agent.qnetwork_local.state_dict(), f'checkpoint_{i_episode}.pth')\n",
        "    return scores\n",
        "\n",
        "scores = dqn()\n",
        "## Tesla T4, linear_256_64:\n",
        "## 2000 episodes, avg score around 15\n",
        "## Wall time: 29min 39s"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZXwpOfAgohD3",
        "outputId": "8c014f68-c438-4ae2-9136-3222d2913216"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Episode 100\tAverage Score: 0.98\n",
            "Episode 200\tAverage Score: 4.38\n",
            "Episode 300\tAverage Score: 8.24\n",
            "Episode 400\tAverage Score: 11.90\n",
            "Episode 500\tAverage Score: 13.18\n",
            "Episode 600\tAverage Score: 13.43\n",
            "Episode 700\tAverage Score: 14.57\n",
            "Episode 800\tAverage Score: 15.24\n",
            "Episode 900\tAverage Score: 15.37\n",
            "Episode 1000\tAverage Score: 16.75\n",
            "Episode 1100\tAverage Score: 15.88\n",
            "Episode 1200\tAverage Score: 16.34\n",
            "Episode 1300\tAverage Score: 15.19\n",
            "Episode 1400\tAverage Score: 15.17\n",
            "Episode 1500\tAverage Score: 15.75\n",
            "Episode 1600\tAverage Score: 15.58\n",
            "Episode 1700\tAverage Score: 15.77\n",
            "Episode 1800\tAverage Score: 15.11\n",
            "Episode 1900\tAverage Score: 15.10\n",
            "Episode 2000\tAverage Score: 14.89\n",
            "CPU times: user 20min 40s, sys: 1min 37s, total: 22min 18s\n",
            "Wall time: 29min 39s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## since the training didn't reach 20, the desired score, no checkpoint has been automatically save.\n",
        "## manually save the model weights.\n",
        "torch.save(agent.qnetwork_local.state_dict(), f'dqn_checkpoint_2000.pth')"
      ],
      "metadata": {
        "id": "6M1IUnaK3sWv"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "# plot the scores\n",
        "fig = plt.figure()\n",
        "ax = fig.add_subplot(111)\n",
        "plt.plot(np.arange(len(scores)), scores)\n",
        "plt.plot(np.arange(len(scores)), pd.DataFrame(scores).rolling(window=100).mean())\n",
        "plt.ylabel('Score')\n",
        "plt.xlabel('Episode #')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "xW7u04YVt8yM",
        "outputId": "49e0ed4c-8b39-4609-d14a-677ce2e71fb7"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **\\<BOTTOM>**  "
      ],
      "metadata": {
        "id": "igVarKO4ZdSz"
      }
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.4"
    },
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}