PPO_gae_curios.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "I8kaAG1UQ5fU"
   },
   "source": [
    "Paper: https://arxiv.org/abs/1705.05363\n",
    "\n",
    "Paper notes:\n",
    "\n",
    "ICM consists of two models: forward and inverse model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "colab_type": "code",
    "id": "m0VNyn5nQNm2",
    "outputId": "ef99e03f-a3a0-4af4-b4d9-c373a5853519"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: box2d-py in /usr/local/lib/python3.6/dist-packages (2.3.8)\n",
      "Requirement already satisfied: gym[Box_2D] in /usr/local/lib/python3.6/dist-packages (0.10.11)\n",
      "\u001b[33m  WARNING: gym 0.10.11 does not provide the extra 'box_2d'\u001b[0m\n",
      "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from gym[Box_2D]) (1.3.1)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from gym[Box_2D]) (1.12.0)\n",
      "Requirement already satisfied: numpy>=1.10.4 in /usr/local/lib/python3.6/dist-packages (from gym[Box_2D]) (1.16.5)\n",
      "Requirement already satisfied: pyglet>=1.2.0 in /usr/local/lib/python3.6/dist-packages (from gym[Box_2D]) (1.4.3)\n",
      "Requirement already satisfied: requests>=2.0 in /usr/local/lib/python3.6/dist-packages (from gym[Box_2D]) (2.21.0)\n",
      "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from pyglet>=1.2.0->gym[Box_2D]) (0.16.0)\n",
      "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.0->gym[Box_2D]) (2.8)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.0->gym[Box_2D]) (3.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.0->gym[Box_2D]) (2019.6.16)\n",
      "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.0->gym[Box_2D]) (1.24.3)\n"
     ]
    }
   ],
   "source": [
    "import gym\n",
    "import math\n",
    "import torch \n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.distributions import Categorical\n",
    "import torch.nn.functional as F\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from torch.nn.utils import clip_grad_norm_\n",
    "from collections import deque\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "DCSiHVc0Q5CN",
    "outputId": "4961aa5d-0091-4382-a4c6-8ed3cc538753"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "action space:  2\n",
      "observation space  4\n",
      "Using:  cpu\n"
     ]
    }
   ],
   "source": [
    "env_name = \"CartPole-v0\"\n",
    "env = gym.make(env_name)\n",
    "\n",
    "\n",
    "print(\"action space: \", env.action_space.n)\n",
    "print(\"observation space \", env.env.observation_space.shape[0])\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "print(\"Using: \", device)\n",
    "\n",
    "GAMMA = 0.99\n",
    "ETA = 1.    # Scaling factor for the influence of the intrinsic Reward\n",
    "BETA = 0.2\n",
    "SCALAR_BETA = 0.1\n",
    "EXTRINSIC_REWARD = False\n",
    "ENTROPY_BONUS = 0.0001\n",
    "LAMBDA = 0.95\n",
    "CLIP_GRAD = .1\n",
    "C_LR = 4e-4\n",
    "A_LR = 4e-4\n",
    "\n",
    "HIDDEN_SIZE = 64\n",
    "\n",
    "class Critic(nn.Module):\n",
    "    def __init__(self, input_shape):\n",
    "        super(Critic, self).__init__()\n",
    "        self.net = nn.Sequential(nn.Linear(input_shape, HIDDEN_SIZE),\n",
    "                                 nn.ReLU(),\n",
    "                                 nn.Linear(HIDDEN_SIZE,HIDDEN_SIZE),\n",
    "                                 nn.ReLU(),\n",
    "                                 nn.Linear(HIDDEN_SIZE, 1))\n",
    "    \n",
    "    def forward(self,x):\n",
    "        x = self.net(x)\n",
    "        return x\n",
    "      \n",
    "class Actor(nn.Module):\n",
    "    def __init__(self, input_shape, output_shape):\n",
    "        super(Actor, self).__init__()\n",
    "        self.net = nn.Sequential(nn.Linear(input_shape, HIDDEN_SIZE),\n",
    "                                 nn.ReLU(),\n",
    "                                 nn.Linear(HIDDEN_SIZE,HIDDEN_SIZE),\n",
    "                                 nn.ReLU(),\n",
    "                                 nn.Linear(HIDDEN_SIZE, output_shape),\n",
    "                                 nn.Softmax(dim=1)\n",
    "                                 )\n",
    "\n",
    "    def forward(self, x):\n",
    "        probs = self.net(x)    \n",
    "        dist = Categorical(probs)\n",
    "        actions = dist.sample()\n",
    "        logprobs = dist.log_prob(actions) \n",
    "    \n",
    "        return actions, logprobs, dist\n",
    "    \n",
    "\n",
    "    \n",
    "class Encoder(nn.Module): \n",
    "    def __init__(self, state_size, enc_state_size=12, hidden_size=64):\n",
    "        super(Encoder, self).__init__()\n",
    "        self.encoder = nn.Sequential(nn.Linear(state_size, hidden_size),\n",
    "                                     nn.ReLU(),\n",
    "                                     nn.Linear(hidden_size, hidden_size),\n",
    "                                     nn.ReLU(),\n",
    "                                     nn.Linear(hidden_size, enc_state_size))\n",
    "  \n",
    "    def forward(self,x):\n",
    "        return self.encoder(x)\n",
    "\n",
    "class Inverse(nn.Module):\n",
    "    \"\"\"\n",
    "    1. (first submodel) encodes the state and next state into feature space.\n",
    "    2. (second submodel) the inverse approximates the action taken by the given state and next state in feature size\n",
    "    \n",
    "    returns the predicted action and the encoded state for the Forward Model and the encoded next state to train the forward model!\n",
    "    \n",
    "    optimizing the Inverse model by the loss between actual action taken by the current policy and the predicted action by the inverse model\n",
    "    \"\"\"\n",
    "    def __init__(self, action_size, enc_state_size, hidden_size=64):\n",
    "        super(Inverse, self).__init__()\n",
    "        self.inverse = nn.Sequential(nn.Linear(enc_state_size*2, hidden_size),\n",
    "                                     nn.ReLU(),\n",
    "                                     nn.Linear(hidden_size, hidden_size),\n",
    "                                     nn.ReLU(),\n",
    "                                     nn.Linear(hidden_size, action_size),\n",
    "                                     nn.Softmax(dim=1))\n",
    "    def forward(self, state1,state2):\n",
    "        x = torch.cat( (state1, state2), dim=1)\n",
    "        return self.inverse(x)\n",
    "\n",
    "    \n",
    "class Forward(nn.Module):\n",
    "  \"\"\"\n",
    "  \n",
    "  \"\"\"\n",
    "    def __init__(self, enc_state_size, OHE_size=12, hidden_size=64):\n",
    "        super(Forward, self).__init__()\n",
    "        self.OHE_size = OHE_size\n",
    "        self.forwardM = nn.Sequential(nn.Linear(enc_state_size+self.OHE_size, hidden_size),\n",
    "                                      nn.ReLU(),\n",
    "                                      nn.Linear(hidden_size, hidden_size),\n",
    "                                      nn.ReLU(),\n",
    "                                      nn.Linear(hidden_size,enc_state_size))\n",
    "    \n",
    "    def forward(self,state,action):\n",
    "        \"\"\"\n",
    "        Gets as inputs the aciton taken from the policy and the encoded state by the encoder in the inverse model.\n",
    "        The froward model trys to predict the encoded next state. \n",
    "        Returns the predicted encoded next state.\n",
    "        Gets optimized by the MSE between the actual encoded next state and the predicted version of the forward model!\n",
    "\n",
    "         \"\"\"\n",
    "        action_ = torch.zeros(action.shape[0],self.OHE_size) # 2024,OHE\n",
    "        indices = torch.stack( (torch.arange(action.shape[0]), action.squeeze().long()), dim=0)\n",
    "        indices = indices.tolist()\n",
    "        action_[indices] = 1.\n",
    "        x = torch.cat( (state,action_) ,dim=1)\n",
    "        return self.forwardM(x)\n",
    "\n",
    "    \n",
    "def ICM(state1, action, state2, forward_scale=1., inverse_scale=1e4):\n",
    "  \"\"\"\n",
    "  \n",
    "  \"\"\"\n",
    "  state1_hat = encoder(state1) \n",
    "  state2_hat = encoder(state2)\n",
    "  \n",
    "  state2_hat_pred = forwardM(state1_hat.detach(), action.detach()) \n",
    "  forward_pred_err = forward_scale * forward_loss(state2_hat_pred, state2_hat.detach()).sum(dim=1).unsqueeze(dim=1)\n",
    "  pred_action = inverse(state1_hat, state2_hat) \n",
    "  \n",
    "\n",
    "  inverse_pred_err = inverse_scale * inverse_loss(pred_action, action.detach().flatten().long()).unsqueeze(dim=1)    \n",
    "  return forward_pred_err, inverse_pred_err"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "M2GM9DziV8I0"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9HbCUq4AQ4-7"
   },
   "outputs": [],
   "source": [
    "def test_net( count = 10):\n",
    "    rewards = 0.0\n",
    "    steps = 0\n",
    "    entropys = 0.0\n",
    "    for _ in range(count):\n",
    "        obs = env.reset()\n",
    "\n",
    "        while True:\n",
    "            obs_v = torch.from_numpy(obs).unsqueeze(0).float()\n",
    "            action, _, dist = actor(obs_v.to(device))\n",
    "            entropy = dist.entropy().detach().cpu().numpy()\n",
    "            obs, reward, done, info = env.step(action[0].cpu().numpy())\n",
    "            \n",
    "            rewards += reward\n",
    "            entropys += entropy.mean()\n",
    "            steps += 1\n",
    "            if done:\n",
    "                break\n",
    "\n",
    "    return rewards/count, entropys/count, steps/count\n",
    "  \n",
    "  \n",
    "  \n",
    "\n",
    "def compute_gae(next_value, rewards, masks, values, gamma=GAMMA, lambda_=LAMBDA):\n",
    "    \"\"\"\n",
    "    lambda => 1: high variance, low bias\n",
    "    lambda => 0: low variance, high bias\n",
    "    \"\"\"\n",
    "    values.append(next_value)\n",
    "    gae = 0\n",
    "    disc_returns = []\n",
    "    advantage = []\n",
    "    for step in reversed(range(len(rewards))):\n",
    "        # d = r_t +gamma*V(s_t+1) - V(s)\n",
    "        delta = rewards[step] + gamma * values[step + 1] * masks[step] - values[step]\n",
    "        # sum(lambda*gamma)^t* delta_t+1\n",
    "        gae = delta + gamma * lambda_ * masks[step] * gae\n",
    "        \n",
    "        disc_returns.insert(0, gae + values[step]) # adding values since we want the returns and not the advantage yet!  A(a,s) = Q\"returns\" - V(s)\n",
    "        advantage.insert(0, gae)\n",
    "        \n",
    "    return torch.FloatTensor(disc_returns).unsqueeze(1), torch.FloatTensor(advantage).unsqueeze(1)\n",
    "\n",
    "  \n",
    "def ppo_iter(mini_batch_size, states, actions, log_probs, advantage, discounted_rewards, curiosity_loss):\n",
    "    batch_size = len(states)\n",
    "    #print(batch_size)\n",
    "    for i in range(batch_size // mini_batch_size):\n",
    "        rand_ids = np.random.randint(0, batch_size, mini_batch_size)\n",
    "        \n",
    "        yield torch.cat(states)[rand_ids], torch.cat(actions)[rand_ids], torch.cat(log_probs)[rand_ids], advantage[rand_ids], discounted_rewards[rand_ids], curiosity_loss[rand_ids]\n",
    "        \n",
    "        \n",
    "\n",
    "def ppo_update(ppo_epochs, mini_batch_size, states, actions, log_probs, advantage, discounted_rewards, curiosity_loss, eps_clip=0.2):\n",
    "  \"\"\"\n",
    "  \n",
    "  \"\"\"\n",
    "\n",
    "  a_loss_batch = []\n",
    "  c_loss_batch = []\n",
    "  icm_loss_batch = []\n",
    "\n",
    "  for _ in range(ppo_epochs):\n",
    "    for states_i, old_actions, old_logprobs, advantage_i, discounted_reward_i, cur_loss  in ppo_iter(mini_batch_size, states, actions, log_probs, advantage, discounted_rewards, curiosity_loss):\n",
    "\n",
    "      optimizer.zero_grad()\n",
    "      #c_optimizer.zero_grad()\n",
    "      #tran critic\n",
    "      new_value = critic(states_i.to(device))\n",
    "      c_loss = .5 * F.mse_loss(new_value, discounted_reward_i)\n",
    "      #clip_grad_norm_(critic.parameters(),CLIP_GRAD)\n",
    "      #c_loss.backward()\n",
    "      #c_optimizer.step()\n",
    "      \n",
    "      c_loss_batch.append(c_loss.detach().numpy())\n",
    "      \n",
    "      \n",
    "      #train actor\n",
    "      #a_optimizer.zero_grad()\n",
    "      _, _, dist = actor(states_i.to(device))\n",
    "      new_logprobs = dist.log_prob(old_actions)\n",
    "      entropy = dist.entropy().mean()\n",
    "\n",
    "      ratio = torch.exp(new_logprobs - old_logprobs.detach())\n",
    "      surr = ratio * advantage_i\n",
    "      clip = torch.clamp(ratio, 1.0 - eps_clip, 1.0 + eps_clip) \n",
    "      a_loss  = - (torch.min(surr, clip*advantage_i).mean()) + ENTROPY_BONUS * entropy\n",
    "      #clip_grad_norm_(actor.parameters(),CLIP_GRAD)\n",
    "      #a_loss.backward()\n",
    "      #a_optimizer.step()\n",
    "      a_loss_batch.append(a_loss.detach().numpy())\n",
    "      \n",
    "      #train icm\n",
    "      #icm_optimizer.zero_grad()\n",
    "      cur_loss = cur_loss.mean()\n",
    "      #cur_loss.backward(retain_graph=True)\n",
    "      #icm_optimizer.step()\n",
    "      \n",
    "      icm_loss_batch.append(cur_loss.detach().numpy())\n",
    "      \n",
    "      #when calculated combined loss:\n",
    "      overall_loss = SCALAR_BETA * (c_loss + a_loss) + cur_loss\n",
    "      overall_loss.backward(retain_graph=True)\n",
    "      optimizer.step()\n",
    "      \n",
    "\n",
    "\n",
    "     \n",
    "  return np.array(c_loss_batch).mean(), np.array(a_loss_batch).mean(), np.array(icm_loss_batch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 516
    },
    "colab_type": "code",
    "id": "6hhsEWZDQ49Z",
    "outputId": "eae8f431-f5c3-4b4b-9ab6-0d2ab393dc94"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode: 550 | Ep_Reward: 170.10 | Average_100: 72.51"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABeIAAAHiCAYAAACEMLwhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n9vfUAdAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1872x576 with 7 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "torch.manual_seed(42)\n",
    "torch.cuda.manual_seed(42)\n",
    "np.random.seed(42)\n",
    "env.seed(42)\n",
    "\n",
    "input_shape  = env.observation_space.shape[0]\n",
    "output_shape = env.action_space.n\n",
    "\n",
    "actor = Actor(input_shape, output_shape).to(device)\n",
    "critic = Critic(input_shape).to(device)\n",
    "\n",
    "\n",
    "encoder = Encoder(state_size=input_shape, enc_state_size=2)\n",
    "inverse = Inverse(action_size=output_shape, enc_state_size=2)\n",
    "forwardM = Forward(enc_state_size=2, OHE_size=2)\n",
    "\n",
    "forward_loss = nn.MSELoss(reduction='none')\n",
    "inverse_loss = nn.CrossEntropyLoss(reduction='none')\n",
    "\n",
    "\n",
    "all_parameters = list(actor.parameters())+list(critic.parameters())+list(encoder.parameters())+list(inverse.parameters())+list(forwardM.parameters())\n",
    "optimizer = optim.RMSprop(params=all_parameters, lr=A_LR)\n",
    "\n",
    "# a_optimizer = optim.RMSprop(params=actor.parameters(), lr=A_LR)\n",
    "# c_optimizer = optim.RMSprop(params=critic.parameters(), lr=C_LR)\n",
    "# icm_params = list(encoder.parameters())+list(inverse.parameters())+list(forwardM.parameters())\n",
    "# icm_optimizer = optim.Adam(params=icm_params, lr = 1e-3)\n",
    "\n",
    "max_episodes = 550\n",
    "\n",
    "\n",
    "c_loss_list = []\n",
    "a_loss_list = []\n",
    "icm_loss_list = []\n",
    "entropy_list = []\n",
    "intrinsic_rewards = []\n",
    "\n",
    "average_100 = []\n",
    "plot_rewards = []\n",
    "max_steps = 2024\n",
    "\n",
    "for ep in range(max_episodes+1):\n",
    "    state = env.reset()\n",
    "    done = False\n",
    "    \n",
    "    state_batch = []\n",
    "    next_state_batch = []\n",
    "    value_batch = []\n",
    "    action_batch = []\n",
    "    logprob_batch = []\n",
    "    rewards_batch = []\n",
    "    masks = []\n",
    "    for step in range(max_steps):\n",
    "\n",
    "        state = torch.from_numpy(state).unsqueeze(0).float()\n",
    "        action, logprob, _  = actor(state.to(device))  \n",
    "        value = critic(state.to(device))\n",
    "        next_state, reward, done, _ = env.step(action[0].cpu().numpy())\n",
    "        \n",
    "        state_batch.append(state)\n",
    "        next_state_batch.append(torch.from_numpy(next_state).unsqueeze(0).float())\n",
    "        value_batch.append(value.item())\n",
    "        logprob_batch.append(logprob)\n",
    "        action_batch.append(action)\n",
    "        rewards_batch.append(reward)  \n",
    "        masks.append(1 - done)\n",
    "\n",
    "        state = next_state\n",
    "\n",
    "        if done:\n",
    "          state = env.reset()\n",
    "\n",
    "       \n",
    "    # Intrinsic Curiosity Calculation\n",
    "    state1_batch = torch.cat(state_batch)\n",
    "    state2_batch = torch.cat(next_state_batch)\n",
    "    actions_batch = torch.cat(action_batch)\n",
    "    forward_pred_err, inverse_pred_err = ICM(state1_batch, actions_batch, state2_batch)\n",
    "    rewards = ((1. / ETA) * forward_pred_err).detach()\n",
    "   \n",
    "    intrinsic_rewards.append(rewards.mean().numpy())\n",
    "    if EXTRINSIC_REWARD == True:\n",
    "        rewards += torch.FloatTensor(rewards_batch).unsqueeze(1)\n",
    "\n",
    "    rewards_batch = list(rewards)  \n",
    "    curiosity_loss = (1 - BETA) * inverse_pred_err + (BETA * forward_pred_err)\n",
    "\n",
    "    # calculate advantage:\n",
    "    next_value = critic(torch.from_numpy(next_state).unsqueeze(0).float()).item()\n",
    "    discounted_rewards, advantage = compute_gae(next_value, rewards_batch, masks, value_batch)\n",
    "    # normalize advantage:\n",
    "    advantage = (advantage - advantage.mean()) / (advantage.std() + 1e-5)\n",
    "    \n",
    "    c_loss, a_loss, icm_loss = ppo_update(ppo_epochs = 5, mini_batch_size = 512, states = state_batch, actions = action_batch, log_probs = logprob_batch, advantage = advantage, discounted_rewards = discounted_rewards, curiosity_loss=curiosity_loss)\n",
    "    c_loss_list.append(c_loss)\n",
    "    a_loss_list.append(a_loss)\n",
    "    icm_loss_list.append(icm_loss)\n",
    "    \n",
    "    \n",
    "    if ep != 0 and ep % 10 == 0:\n",
    "        test_rewards, test_entropy, test_steps = test_net()\n",
    "        entropy_list.append(test_entropy)\n",
    "        plot_rewards.append(test_rewards)\n",
    "\n",
    "        average_100.append(np.mean(plot_rewards[-100:]))\n",
    "        print(\"\\rEpisode: {} | Ep_Reward: {:.2f} | Average_100: {:.2f}\".format(ep, test_rewards, np.mean(plot_rewards[-100:])), end = \"\", flush = True)\n",
    "        \n",
    "        \n",
    "\n",
    "    \n",
    "\n",
    "# PLOTTING RESULTS\n",
    "\n",
    "plt.figure(figsize = (26,8))\n",
    "plt.subplot(1,7,1)\n",
    "plt.title(\"actor loss\")\n",
    "plt.plot(a_loss_list)\n",
    "plt.subplot(1,7,2)\n",
    "plt.title(\"critic loss\")\n",
    "plt.plot(c_loss_list)\n",
    "plt.subplot(1,7,3)\n",
    "plt.title(\"ICM loss\")\n",
    "plt.plot(icm_loss_list)\n",
    "plt.subplot(1,7,4)\n",
    "plt.title(\"entropy\")\n",
    "plt.plot(entropy_list)\n",
    "plt.subplot(1,7,5)\n",
    "plt.title(\"rewards\")\n",
    "plt.plot(plot_rewards)\n",
    "plt.subplot(1,7,6)\n",
    "plt.title(\"intrinsic rewards\")\n",
    "plt.plot(intrinsic_rewards)\n",
    "plt.subplot(1,7,7)\n",
    "plt.title(\"Average100\")\n",
    "plt.plot(average_100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hKHSlX0MphbB"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pQf9o4WnmzUj"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "f2HTfJKdgobJ"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "PPO_gae_curios",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}