notebooks/101_train-decision-transformers.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/101_train-decision-transformers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FJnwpdtXTuF7"
      },
      "source": [
        "# Training Decision Transformers with 🤗 transformers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TmRpOylHmoSo"
      },
      "source": [
        "In this tutorial, **you’ll learn to train your first Offline Decision Transformer model from scratch to make a half-cheetah run.** 🏃\n",
        "\n",
        "❓ If you have questions, please post them on #study-group discord channel 👉 https://discord.gg/aYka4Yhff9\n",
        "\n",
        "🎮 Environments:\n",
        "- [Half Cheetah](https://www.gymlibrary.dev/environments/mujoco/half_cheetah/)\n",
        "\n",
        "⬇️ Here's what you'll achieve at the end of this tutorial. ⬇️"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "I30mBweWvTz6"
      },
      "outputs": [],
      "source": [
        "%%html\n",
        "<video controls autoplay><source src=\"https://huggingface.co/edbeeching/decision-transformer-gym-halfcheetah-expert/resolve/main/replay.mp4\" type=\"video/mp4\"></video>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h78SBsjCviBm"
      },
      "source": [
        "### Prerequisites 🏗️\n",
        "Before diving into the notebook, you need to:\n",
        "\n",
        "🔲 📚 [Read the tutorial](https://huggingface.co/blog/train-decision-transformers)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7lROBz6Ov64C"
      },
      "source": [
        "### Step 0: Set the GPU 💪\n",
        "- To **faster the agent's training, we'll use a GPU** to do that go to `Runtime > Change Runtime type`\n",
        "![image.png](data:image/png;base64,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)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cBTys2Ctv7zQ"
      },
      "source": [
        "- `Hardware Accelerator > GPU`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0NJAPy7Dv-te"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WCsT6KVxwARY"
      },
      "source": [
        "### Step 1: Install dependencies for model evaluation 🔽"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4qntpWiEWX89"
      },
      "outputs": [],
      "source": [
        "!apt-get install -y \\\n",
        "    libgl1-mesa-dev \\\n",
        "    libgl1-mesa-glx \\\n",
        "    libglew-dev \\\n",
        "    libosmesa6-dev \\\n",
        "    software-properties-common \\\n",
        "    patchelf \\\n",
        "    xvfb"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qr-1h83zVsMK"
      },
      "source": [
        "### Step 2: Install and import the packages 📦\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RuXIPJUWVrhq"
      },
      "outputs": [],
      "source": [
        "!pip install gym==0.21.0\n",
        "!pip install free-mujoco-py\n",
        "!pip install transformers\n",
        "!pip install datasets\n",
        "!pip install imageio-ffmpeg\n",
        "\n",
        "!pip install colabgymrender==1.0.2\n",
        "!pip install xvfbwrapper\n",
        "!pip install imageio==2.4.1\n",
        "!pip install imageio-ffmpeg\n",
        "!pip install huggingface_hub\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DktITQNXTopc"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import random\n",
        "from dataclasses import dataclass\n",
        "\n",
        "import numpy as np\n",
        "import torch\n",
        "from datasets import load_dataset\n",
        "from transformers import DecisionTransformerConfig, DecisionTransformerModel, Trainer, TrainingArguments"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A1Ugq2POUmRA"
      },
      "source": [
        "### Step 3: Loading the dataset from the 🤗 Hub and instantiating the model\n",
        "\n",
        "We host a number of Offline RL Datasets on the hub. Today we will be training with the halfcheetah “expert” dataset, hosted here on hub.\n",
        "\n",
        "First we need to import the load_dataset function from the 🤗 datasets package and download the dataset to our machine."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s3bLeIHqUwq7"
      },
      "outputs": [],
      "source": [
        "os.environ[\"WANDB_DISABLED\"] = \"true\" # we diable weights and biases logging for this tutorial\n",
        "dataset = load_dataset(\"edbeeching/decision_transformer_gym_replay\", \"halfcheetah-expert-v2\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZFmTdHoHUD13"
      },
      "source": [
        "### Step 4: Defining a custom DataCollator for the transformers Trainer class"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "l1QzZHmPUM4p"
      },
      "outputs": [],
      "source": [
        "@dataclass\n",
        "class DecisionTransformerGymDataCollator:\n",
        "    return_tensors: str = \"pt\"\n",
        "    max_len: int = 20 #subsets of the episode we use for training\n",
        "    state_dim: int = 17  # size of state space\n",
        "    act_dim: int = 6  # size of action space\n",
        "    max_ep_len: int = 1000 # max episode length in the dataset\n",
        "    scale: float = 1000.0  # normalization of rewards/returns\n",
        "    state_mean: np.array = None  # to store state means\n",
        "    state_std: np.array = None  # to store state stds\n",
        "    p_sample: np.array = None  # a distribution to take account trajectory lengths\n",
        "    n_traj: int = 0 # to store the number of trajectories in the dataset\n",
        "\n",
        "    def __init__(self, dataset) -> None:\n",
        "        self.act_dim = len(dataset[0][\"actions\"][0])\n",
        "        self.state_dim = len(dataset[0][\"observations\"][0])\n",
        "        self.dataset = dataset\n",
        "        # calculate dataset stats for normalization of states\n",
        "        states = []\n",
        "        traj_lens = []\n",
        "        for obs in dataset[\"observations\"]:\n",
        "            states.extend(obs)\n",
        "            traj_lens.append(len(obs))\n",
        "        self.n_traj = len(traj_lens)\n",
        "        states = np.vstack(states)\n",
        "        self.state_mean, self.state_std = np.mean(states, axis=0), np.std(states, axis=0) + 1e-6\n",
        "        \n",
        "        traj_lens = np.array(traj_lens)\n",
        "        self.p_sample = traj_lens / sum(traj_lens)\n",
        "\n",
        "    def _discount_cumsum(self, x, gamma):\n",
        "        discount_cumsum = np.zeros_like(x)\n",
        "        discount_cumsum[-1] = x[-1]\n",
        "        for t in reversed(range(x.shape[0] - 1)):\n",
        "            discount_cumsum[t] = x[t] + gamma * discount_cumsum[t + 1]\n",
        "        return discount_cumsum\n",
        "\n",
        "    def __call__(self, features):\n",
        "        batch_size = len(features)\n",
        "        # this is a bit of a hack to be able to sample of a non-uniform distribution\n",
        "        batch_inds = np.random.choice(\n",
        "            np.arange(self.n_traj),\n",
        "            size=batch_size,\n",
        "            replace=True,\n",
        "            p=self.p_sample,  # reweights so we sample according to timesteps\n",
        "        )\n",
        "        # a batch of dataset features\n",
        "        s, a, r, d, rtg, timesteps, mask = [], [], [], [], [], [], []\n",
        "        \n",
        "        for ind in batch_inds:\n",
        "            # for feature in features:\n",
        "            feature = self.dataset[int(ind)]\n",
        "            si = random.randint(0, len(feature[\"rewards\"]) - 1)\n",
        "\n",
        "            # get sequences from dataset\n",
        "            s.append(np.array(feature[\"observations\"][si : si + self.max_len]).reshape(1, -1, self.state_dim))\n",
        "            a.append(np.array(feature[\"actions\"][si : si + self.max_len]).reshape(1, -1, self.act_dim))\n",
        "            r.append(np.array(feature[\"rewards\"][si : si + self.max_len]).reshape(1, -1, 1))\n",
        "\n",
        "            d.append(np.array(feature[\"dones\"][si : si + self.max_len]).reshape(1, -1))\n",
        "            timesteps.append(np.arange(si, si + s[-1].shape[1]).reshape(1, -1))\n",
        "            timesteps[-1][timesteps[-1] >= self.max_ep_len] = self.max_ep_len - 1  # padding cutoff\n",
        "            rtg.append(\n",
        "                self._discount_cumsum(np.array(feature[\"rewards\"][si:]), gamma=1.0)[\n",
        "                    : s[-1].shape[1]   # TODO check the +1 removed here\n",
        "                ].reshape(1, -1, 1)\n",
        "            )\n",
        "            if rtg[-1].shape[1] < s[-1].shape[1]:\n",
        "                print(\"if true\")\n",
        "                rtg[-1] = np.concatenate([rtg[-1], np.zeros((1, 1, 1))], axis=1)\n",
        "\n",
        "            # padding and state + reward normalization\n",
        "            tlen = s[-1].shape[1]\n",
        "            s[-1] = np.concatenate([np.zeros((1, self.max_len - tlen, self.state_dim)), s[-1]], axis=1)\n",
        "            s[-1] = (s[-1] - self.state_mean) / self.state_std\n",
        "            a[-1] = np.concatenate(\n",
        "                [np.ones((1, self.max_len - tlen, self.act_dim)) * -10.0, a[-1]],\n",
        "                axis=1,\n",
        "            )\n",
        "            r[-1] = np.concatenate([np.zeros((1, self.max_len - tlen, 1)), r[-1]], axis=1)\n",
        "            d[-1] = np.concatenate([np.ones((1, self.max_len - tlen)) * 2, d[-1]], axis=1)\n",
        "            rtg[-1] = np.concatenate([np.zeros((1, self.max_len - tlen, 1)), rtg[-1]], axis=1) / self.scale\n",
        "            timesteps[-1] = np.concatenate([np.zeros((1, self.max_len - tlen)), timesteps[-1]], axis=1)\n",
        "            mask.append(np.concatenate([np.zeros((1, self.max_len - tlen)), np.ones((1, tlen))], axis=1))\n",
        "\n",
        "        s = torch.from_numpy(np.concatenate(s, axis=0)).float()\n",
        "        a = torch.from_numpy(np.concatenate(a, axis=0)).float()\n",
        "        r = torch.from_numpy(np.concatenate(r, axis=0)).float()\n",
        "        d = torch.from_numpy(np.concatenate(d, axis=0))\n",
        "        rtg = torch.from_numpy(np.concatenate(rtg, axis=0)).float()\n",
        "        timesteps = torch.from_numpy(np.concatenate(timesteps, axis=0)).long()\n",
        "        mask = torch.from_numpy(np.concatenate(mask, axis=0)).float()\n",
        "\n",
        "        return {\n",
        "            \"states\": s,\n",
        "            \"actions\": a,\n",
        "            \"rewards\": r,\n",
        "            \"returns_to_go\": rtg,\n",
        "            \"timesteps\": timesteps,\n",
        "            \"attention_mask\": mask,\n",
        "        }"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bmTRGPKYUVFG"
      },
      "source": [
        "### Step 5: Extending the Decision Transformer Model to include a loss function\n",
        "\n",
        "In order to train the model with the 🤗 trainer class, we first need to ensure the dictionary it returns contains a loss, in this case L-2 norm of the models action predictions and the targets."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bwZp7hhFUh5u"
      },
      "outputs": [],
      "source": [
        "class TrainableDT(DecisionTransformerModel):\n",
        "    def __init__(self, config):\n",
        "        super().__init__(config)\n",
        "\n",
        "    def forward(self, **kwargs):\n",
        "        output = super().forward(**kwargs)\n",
        "        # add the DT loss\n",
        "        action_preds = output[1]\n",
        "        action_targets = kwargs[\"actions\"]\n",
        "        attention_mask = kwargs[\"attention_mask\"]\n",
        "        act_dim = action_preds.shape[2]\n",
        "        action_preds = action_preds.reshape(-1, act_dim)[attention_mask.reshape(-1) > 0]\n",
        "        action_targets = action_targets.reshape(-1, act_dim)[attention_mask.reshape(-1) > 0]\n",
        "        \n",
        "        loss = torch.mean((action_preds - action_targets) ** 2)\n",
        "\n",
        "        return {\"loss\": loss}\n",
        "\n",
        "    def original_forward(self, **kwargs):\n",
        "        return super().forward(**kwargs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zIJCY3b3pQAh"
      },
      "outputs": [],
      "source": [
        "collator = DecisionTransformerGymDataCollator(dataset[\"train\"])\n",
        "\n",
        "config = DecisionTransformerConfig(state_dim=collator.state_dim, act_dim=collator.act_dim)\n",
        "model = TrainableDT(config)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nJJ2mr_cU4eE"
      },
      "source": [
        "### Step 6: Defining the training hyperparameters and training the model\n",
        "Here, we define the training hyperparameters and our Trainer class that we'll use to train our Decision Transformer model.\n",
        "\n",
        "This step takes about an hour, so you may leave it running. Note the authors train for at least 3 hours, so the results presented here are not as performant as the models hosted on the 🤗 hub."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nNzzKWuuU9I4"
      },
      "outputs": [],
      "source": [
        "training_args = TrainingArguments(\n",
        "    output_dir=\"output/\",\n",
        "    remove_unused_columns=False,\n",
        "    num_train_epochs=120,\n",
        "    per_device_train_batch_size=64,\n",
        "    learning_rate=1e-4,\n",
        "    weight_decay=1e-4,\n",
        "    warmup_ratio=0.1,\n",
        "    optim=\"adamw_torch\",\n",
        "    max_grad_norm=0.25,\n",
        ")\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=dataset[\"train\"],\n",
        "    data_collator=collator,\n",
        ")\n",
        "\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sNaj6bOkp3bt"
      },
      "source": [
        "### Step 7: Visualize the performance of the agent"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y0NhIn4up26c"
      },
      "outputs": [],
      "source": [
        "import mujoco_py\n",
        "import gym\n",
        "\n",
        "from colabgymrender.recorder import Recorder"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T-izl68BqUG5"
      },
      "outputs": [],
      "source": [
        "# Function that gets an action from the model using autoregressive prediction with a window of the previous 20 timesteps.\n",
        "def get_action(model, states, actions, rewards, returns_to_go, timesteps):\n",
        "    # This implementation does not condition on past rewards\n",
        "\n",
        "    states = states.reshape(1, -1, model.config.state_dim)\n",
        "    actions = actions.reshape(1, -1, model.config.act_dim)\n",
        "    returns_to_go = returns_to_go.reshape(1, -1, 1)\n",
        "    timesteps = timesteps.reshape(1, -1)\n",
        "\n",
        "    states = states[:, -model.config.max_length :]\n",
        "    actions = actions[:, -model.config.max_length :]\n",
        "    returns_to_go = returns_to_go[:, -model.config.max_length :]\n",
        "    timesteps = timesteps[:, -model.config.max_length :]\n",
        "    padding = model.config.max_length - states.shape[1]\n",
        "    # pad all tokens to sequence length\n",
        "    attention_mask = torch.cat([torch.zeros(padding), torch.ones(states.shape[1])])\n",
        "    attention_mask = attention_mask.to(dtype=torch.long).reshape(1, -1)\n",
        "    states = torch.cat([torch.zeros((1, padding, model.config.state_dim)), states], dim=1).float()\n",
        "    actions = torch.cat([torch.zeros((1, padding, model.config.act_dim)), actions], dim=1).float()\n",
        "    returns_to_go = torch.cat([torch.zeros((1, padding, 1)), returns_to_go], dim=1).float()\n",
        "    timesteps = torch.cat([torch.zeros((1, padding), dtype=torch.long), timesteps], dim=1)\n",
        "\n",
        "    state_preds, action_preds, return_preds = model.original_forward(\n",
        "        states=states,\n",
        "        actions=actions,\n",
        "        rewards=rewards,\n",
        "        returns_to_go=returns_to_go,\n",
        "        timesteps=timesteps,\n",
        "        attention_mask=attention_mask,\n",
        "        return_dict=False,\n",
        "    )\n",
        "\n",
        "    return action_preds[0, -1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TFPuiNy-qWnP"
      },
      "outputs": [],
      "source": [
        "# build the environment\n",
        "directory = './video'\n",
        "model = model.to(\"cpu\")\n",
        "env = gym.make(\"HalfCheetah-v3\")\n",
        "env = Recorder(env, directory, fps=30)\n",
        "max_ep_len = 1000\n",
        "device = \"cpu\"\n",
        "scale = 1000.0  # normalization for rewards/returns\n",
        "TARGET_RETURN = 12000 / scale  # evaluation is conditioned on a return of 12000, scaled accordingly\n",
        "\n",
        "state_mean = collator.state_mean.astype(np.float32)\n",
        "state_std = collator.state_std.astype(np.float32)\n",
        "print(state_mean)\n",
        "\n",
        "state_dim = env.observation_space.shape[0]\n",
        "act_dim = env.action_space.shape[0]\n",
        "# Create the decision transformer model\n",
        "\n",
        "state_mean = torch.from_numpy(state_mean).to(device=device)\n",
        "state_std = torch.from_numpy(state_std).to(device=device)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ysPH9rtRqY-g"
      },
      "outputs": [],
      "source": [
        "# Interact with the environment and create a video\n",
        "episode_return, episode_length = 0, 0\n",
        "state = env.reset()\n",
        "target_return = torch.tensor(TARGET_RETURN, device=device, dtype=torch.float32).reshape(1, 1)\n",
        "states = torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.float32)\n",
        "actions = torch.zeros((0, act_dim), device=device, dtype=torch.float32)\n",
        "rewards = torch.zeros(0, device=device, dtype=torch.float32)\n",
        "\n",
        "timesteps = torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)\n",
        "for t in range(max_ep_len):\n",
        "    actions = torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)\n",
        "    rewards = torch.cat([rewards, torch.zeros(1, device=device)])\n",
        "\n",
        "    action = get_action(\n",
        "        model,\n",
        "        (states - state_mean) / state_std,\n",
        "        actions,\n",
        "        rewards,\n",
        "        target_return,\n",
        "        timesteps,\n",
        "    )\n",
        "    actions[-1] = action\n",
        "    action = action.detach().cpu().numpy()\n",
        "\n",
        "    state, reward, done, _ = env.step(action)\n",
        "\n",
        "    cur_state = torch.from_numpy(state).to(device=device).reshape(1, state_dim)\n",
        "    states = torch.cat([states, cur_state], dim=0)\n",
        "    rewards[-1] = reward\n",
        "\n",
        "    pred_return = target_return[0, -1] - (reward / scale)\n",
        "    target_return = torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)\n",
        "    timesteps = torch.cat([timesteps, torch.ones((1, 1), device=device, dtype=torch.long) * (t + 1)], dim=1)\n",
        "\n",
        "    episode_return += reward\n",
        "    episode_length += 1\n",
        "\n",
        "    if done:\n",
        "        break\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "dn1Zy7x9qbnI"
      },
      "outputs": [],
      "source": [
        "# Play the video\n",
        "env.play()\n",
        "# If you want to convert the video:\n",
        "# !ffmpeg -i {your_video} -vcodec h264 replay.mp4 "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d0MTM3o5xJf8"
      },
      "source": [
        "### Step 8: Publish our trained model on the Hub 🔥\n",
        "Now that we saw we got good results after the training, we can publish our trained model on the hub 🤗 with one line of code.\n",
        "\n",
        "Under the hood, the Hub uses git-based repositories (don't worry if you don't know what git is), which means you can update the model with new versions as you experiment and improve your agent."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hLFQfIXLxPpq"
      },
      "source": [
        "To be able to share your model with the community there are three more steps to follow:\n",
        "\n",
        "1️⃣ (If it's not already done) create an account to HF ➡ https://huggingface.co/join\n",
        "\n",
        "2️⃣ Sign in and then, you need to store your authentication token from the Hugging Face website.\n",
        "- Create a new token (https://huggingface.co/settings/tokens) **with write role**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2IJbhQvrxRVD"
      },
      "source": [
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "zkuqy2bdxUdj"
      },
      "outputs": [],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hIBqBv95xS7M"
      },
      "source": [
        "If you don't want to use a Google Colab or a Jupyter Notebook, you need to use this command instead: `huggingface-cli login`\n",
        "\n",
        "3️⃣ We're now ready to push our trained model to the 🤗 Hub 🔥 !!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "vZwU9gfMxbht"
      },
      "outputs": [],
      "source": [
        "trainer.push_to_hub()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7EgwxEjOxkBS"
      },
      "source": [
        "## Some additional challenges 🏆\n",
        "Congratulations, you've just trained your first Decision Transformer 🥳.\n",
        "\n",
        "Now, the best way to learn **is to try things by your own**! Why not trying with another environment?\n",
        "\n",
        "We provide datasets for some other environments:\n",
        "* [Walker2D](https://huggingface.co/edbeeching/decision-transformer-gym-walker2d-expert)\n",
        "* [Hopper](https://huggingface.co/edbeeching/decision-transformer-gym-hopper-expert)\n",
        "\n",
        "\n",
        "Have fun!\n",
        "\n",
        "### Keep Learning, Stay awesome 🤗\n"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "provenance": [],
      "private_outputs": true,
      "include_colab_link": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}