PPO_conti_gae_curio_multi.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "PPO_conti_gae_curio_multi.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "vJkwdUkfbvQr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "bce57d57-b474-419e-afa3-2d369fa3989f"
      },
      "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 Normal\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",
        "\n",
        "!pip install box2d-py\n",
        "!pip install gym[Box_2D]"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting box2d-py\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/06/bd/6cdc3fd994b0649dcf5d9bad85bd9e26172308bbe9a421bfc6fdbf5081a6/box2d_py-2.3.8-cp36-cp36m-manylinux1_x86_64.whl (448kB)\n",
            "\u001b[K     |████████████████████████████████| 450kB 2.3MB/s \n",
            "\u001b[?25hInstalling collected packages: box2d-py\n",
            "Successfully installed box2d-py-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: 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: six in /usr/local/lib/python3.6/dist-packages (from gym[Box_2D]) (1.12.0)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from gym[Box_2D]) (1.3.1)\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: 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: 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: 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: 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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oJndP12tsVw7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from multiprocessing import Process, Pipe\n",
        "import numpy as np\n",
        "\n",
        "def worker(remote, parent_remote, env_fn_wrapper):\n",
        "    parent_remote.close()\n",
        "    env = env_fn_wrapper.x()\n",
        "    while True:\n",
        "        cmd, data = remote.recv()\n",
        "        if cmd == 'step':\n",
        "            ob, reward, done, info = env.step(data)\n",
        "            if done:\n",
        "                ob = env.reset()\n",
        "            remote.send((ob, reward, done, info))\n",
        "        elif cmd == 'reset':\n",
        "            ob = env.reset()\n",
        "            remote.send(ob)\n",
        "        elif cmd == 'reset_task':\n",
        "            ob = env.reset_task()\n",
        "            remote.send(ob)\n",
        "        elif cmd == 'close':\n",
        "            remote.close()\n",
        "            break\n",
        "        elif cmd == 'get_spaces':\n",
        "            remote.send((env.observation_space, env.action_space))\n",
        "        else:\n",
        "            raise NotImplementedError\n",
        "\n",
        "class CloudpickleWrapper(object):\n",
        "    \"\"\"\n",
        "    Uses cloudpickle to serialize contents (otherwise multiprocessing tries to use pickle)\n",
        "    \"\"\"\n",
        "    def __init__(self, x):\n",
        "        self.x = x\n",
        "    def __getstate__(self):\n",
        "        import cloudpickle\n",
        "        return cloudpickle.dumps(self.x)\n",
        "    def __setstate__(self, ob):\n",
        "        import pickle\n",
        "        self.x = pickle.loads(ob)\n",
        "        \n",
        "\n",
        "class VecEnv(object):\n",
        "    \"\"\"\n",
        "    An abstract asynchronous, vectorized environment.\n",
        "    \"\"\"\n",
        "    def __init__(self, num_envs, observation_space, action_space):\n",
        "        self.num_envs = num_envs\n",
        "        self.observation_space = observation_space\n",
        "        self.action_space = action_space\n",
        "\n",
        "    def reset(self):\n",
        "        \"\"\"\n",
        "        Reset all the environments and return an array of\n",
        "        observations, or a tuple of observation arrays.\n",
        "        If step_async is still doing work, that work will\n",
        "        be cancelled and step_wait() should not be called\n",
        "        until step_async() is invoked again.\n",
        "        \"\"\"\n",
        "        pass\n",
        "\n",
        "    def step_async(self, actions):\n",
        "        \"\"\"\n",
        "        Tell all the environments to start taking a step\n",
        "        with the given actions.\n",
        "        Call step_wait() to get the results of the step.\n",
        "        You should not call this if a step_async run is\n",
        "        already pending.\n",
        "        \"\"\"\n",
        "        pass\n",
        "\n",
        "    def step_wait(self):\n",
        "        \"\"\"\n",
        "        Wait for the step taken with step_async().\n",
        "        Returns (obs, rews, dones, infos):\n",
        "         - obs: an array of observations, or a tuple of\n",
        "                arrays of observations.\n",
        "         - rews: an array of rewards\n",
        "         - dones: an array of \"episode done\" booleans\n",
        "         - infos: a sequence of info objects\n",
        "        \"\"\"\n",
        "        pass\n",
        "\n",
        "    def close(self):\n",
        "        \"\"\"\n",
        "        Clean up the environments' resources.\n",
        "        \"\"\"\n",
        "        pass\n",
        "\n",
        "    def step(self, actions):\n",
        "        self.step_async(actions)\n",
        "        return self.step_wait()\n",
        "\n",
        "class SubprocVecEnv(VecEnv):\n",
        "    def __init__(self, env_fns, spaces=None):\n",
        "        \"\"\"\n",
        "        envs: list of gym environments to run in subprocesses\n",
        "        \"\"\"\n",
        "        self.waiting = False\n",
        "        self.closed = False\n",
        "        nenvs = len(env_fns)\n",
        "        self.nenvs = nenvs\n",
        "        self.remotes, self.work_remotes = zip(*[Pipe() for _ in range(nenvs)])\n",
        "        self.ps = [Process(target=worker, args=(work_remote, remote, CloudpickleWrapper(env_fn)))\n",
        "            for (work_remote, remote, env_fn) in zip(self.work_remotes, self.remotes, env_fns)]\n",
        "        for p in self.ps:\n",
        "            p.daemon = True # if the main process crashes, we should not cause things to hang\n",
        "            p.start()\n",
        "        for remote in self.work_remotes:\n",
        "            remote.close()\n",
        "\n",
        "        self.remotes[0].send(('get_spaces', None))\n",
        "        observation_space, action_space = self.remotes[0].recv()\n",
        "        VecEnv.__init__(self, len(env_fns), observation_space, action_space)\n",
        "\n",
        "    def step_async(self, actions):\n",
        "        for remote, action in zip(self.remotes, actions):\n",
        "            remote.send(('step', action))\n",
        "        self.waiting = True\n",
        "\n",
        "    def step_wait(self):\n",
        "        results = [remote.recv() for remote in self.remotes]\n",
        "        self.waiting = False\n",
        "        obs, rews, dones, infos = zip(*results)\n",
        "        return np.stack(obs), np.stack(rews), np.stack(dones), infos\n",
        "\n",
        "    def reset(self):\n",
        "        for remote in self.remotes:\n",
        "            remote.send(('reset', None))\n",
        "        return np.stack([remote.recv() for remote in self.remotes])\n",
        "\n",
        "    def reset_task(self):\n",
        "        for remote in self.remotes:\n",
        "            remote.send(('reset_task', None))\n",
        "        return np.stack([remote.recv() for remote in self.remotes])\n",
        "\n",
        "    def close(self):\n",
        "        if self.closed:\n",
        "            return\n",
        "        if self.waiting:\n",
        "            for remote in self.remotes:            \n",
        "                remote.recv()\n",
        "        for remote in self.remotes:\n",
        "            remote.send(('close', None))\n",
        "        for p in self.ps:\n",
        "            p.join()\n",
        "            self.closed = True\n",
        "            \n",
        "    def __len__(self):\n",
        "        return self.nenvs"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cJpiz1jlcBSt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "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",
        "        self.mean = nn.Linear(HIDDEN_SIZE, output_shape)\n",
        "        self.variance = nn.Linear(HIDDEN_SIZE, output_shape)\n",
        "        \n",
        "\n",
        "    def forward(self, x):\n",
        "        \n",
        "        head = self.net(x)\n",
        "        mean = F.tanh(self.mean(head)) # tanh squashed output to the range of -1..1\n",
        "        variance = F.softplus(self.variance(head)) # log(1 + e^x) has the shape of a smoothed ReLU\n",
        "        sigma = torch.sqrt(variance.cpu())\n",
        "        m = Normal(mean.cpu(), sigma)\n",
        "        actions = m.sample()\n",
        "        actions = torch.clamp(actions, -1, 1) # usually clipping between -1,1 but pendulum env has action range of -2,2\n",
        "\n",
        "        logprobs = m.log_prob(actions) #for the optimization step we create a new distribution based on the new mean and variance - still taking the logprobs based on the old actions!\n",
        "\n",
        "    \n",
        "        return actions, logprobs, m\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",
        "  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",
        "    x = F.tanh(self.inverse(x))\n",
        "    #x = torch.clamp(x, -2, 2) # usually tanh clips between -1,1 but pendulum env has action range of -2,2\n",
        "    return x\n",
        "  \n",
        "  \n",
        "class Forward(nn.Module):\n",
        "  \"\"\"\n",
        "  \n",
        "  \"\"\"\n",
        "  def __init__(self, enc_state_size, action_size, hidden_size=64):\n",
        "    super(Forward, self).__init__()\n",
        "\n",
        "    self.forwardM = nn.Sequential(nn.Linear(enc_state_size+action_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",
        "    #print(action)\n",
        "    x = torch.cat((state,action), dim=1)\n",
        "    return self.forwardM(x)\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U9bO4hPLcWTw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Agent():\n",
        "  def __init__(self,\n",
        "               state_size,\n",
        "               action_size,\n",
        "               hidden_size,\n",
        "               extrinsic_reward=True,\n",
        "               LR=1e-4,\n",
        "               gamma=0.99,\n",
        "               lambda_=0.95,\n",
        "               mini_batch_size=512,\n",
        "               ppo_epochs=5):\n",
        "    \n",
        "    \n",
        "    self.actor = Actor(state_size, action_size).to(device)\n",
        "    self.critic = Critic(state_size).to(device)\n",
        "    \n",
        "    self.gamma = gamma\n",
        "    self.lambda_ = lambda_\n",
        "    self.mini_batch_size = mini_batch_size\n",
        "    self.ppo_epochs = ppo_epochs\n",
        "    \n",
        "    self.encoder = Encoder(state_size=state_size, enc_state_size=4)\n",
        "    self.inverse = Inverse(action_size=action_size, enc_state_size=4)\n",
        "    self.forwardM = Forward(enc_state_size=4, action_size=action_size)\n",
        "\n",
        "    self.forward_loss = nn.MSELoss(reduction='none')\n",
        "    self.inverse_loss = nn.PairwiseDistance()\n",
        "\n",
        "\n",
        "    self.all_parameters = list(self.actor.parameters())+list(self.critic.parameters())+list(self.encoder.parameters())+list(self.inverse.parameters())+list(self.forwardM.parameters())\n",
        "    self.optimizer = optim.RMSprop(params=self.all_parameters, lr=LR)\n",
        "    \n",
        "  def ICM(self, state1, action, state2, forward_scale=1., inverse_scale=1e4):\n",
        "    \"\"\"\n",
        "\n",
        "    \"\"\"\n",
        "    state1_hat = self.encoder(state1) \n",
        "    state2_hat = self.encoder(state2)\n",
        "\n",
        "    state2_hat_pred = self.forwardM(state1_hat.detach(), action.detach()) \n",
        "    forward_pred_err = forward_scale * self.forward_loss(state2_hat_pred, state2_hat.detach()).sum(dim=1).unsqueeze(dim=1)\n",
        "    pred_action = self.inverse(state1_hat, state2_hat) \n",
        "\n",
        "\n",
        "    inverse_pred_err = inverse_scale * self.inverse_loss(pred_action, action.detach()).unsqueeze(dim=1)    \n",
        "    return forward_pred_err, inverse_pred_err\n",
        "    \n",
        "\n",
        "  def test_net(self, count = 10):\n",
        "      \"\"\"\n",
        "      Tests the agents performance with current weights.\n",
        "      \"\"\"\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 = self.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(self, next_value, rewards, masks, values):\n",
        "      \"\"\"\n",
        "      lambda => 1: high variance, low bias\n",
        "      lambda => 0: low variance, high bias\n",
        "      \"\"\"\n",
        "#       print(rewards)\n",
        "#       print(masks)\n",
        "      rewards_batch = rewards\n",
        "      masks_batch = list(zip(*masks))\n",
        "      values_batch = torch.cat((torch.stack(values, dim=1).squeeze(2), next_value.squeeze(0)),dim=1)\n",
        "      \n",
        "#       print(\"rewards:\",rewards_batch)\n",
        "#       print(\"masks;\", masks_batch)\n",
        "#       print(\"values_batch: \",values_batch)\n",
        "      \n",
        "      out_discounted_rewards = []\n",
        "      out_advantage = []\n",
        "      for rewards, masks, values  in zip(rewards_batch, masks_batch, values_batch):\n",
        "      \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] + self.gamma * values[step + 1] * masks[step] - values[step]\n",
        "            # sum(lambda*gamma)^t* delta_t+1\n",
        "            gae = delta + self.gamma * self.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",
        "        out_discounted_rewards.append(disc_returns)\n",
        "        out_advantage.append(advantage)\n",
        "        \n",
        "      return torch.FloatTensor(out_discounted_rewards).flatten().unsqueeze(1), torch.FloatTensor(out_advantage).flatten().unsqueeze(1)\n",
        "\n",
        "\n",
        "  def ppo_iter(self, states, actions, log_probs, advantage, discounted_rewards, curiosity_loss):\n",
        "      batch_size = len(states)\n",
        "\n",
        "      for i in range(batch_size // self.mini_batch_size):\n",
        "          rand_ids = np.random.randint(0, batch_size, self.mini_batch_size)\n",
        "\n",
        "          yield states[rand_ids], actions[rand_ids], log_probs[rand_ids], advantage[rand_ids], discounted_rewards[rand_ids], curiosity_loss[rand_ids]\n",
        "\n",
        "\n",
        "\n",
        "  def ppo_update(self, 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(self.ppo_epochs):\n",
        "      for states_i, old_actions, old_logprobs, advantage_i, discounted_reward_i, cur_loss  in self.ppo_iter(states, actions, log_probs, advantage, discounted_rewards, curiosity_loss):\n",
        "\n",
        "        self.optimizer.zero_grad()\n",
        "\n",
        "        #train critic\n",
        "        new_value = self.critic(states_i.to(device))\n",
        "        c_loss = .5 * F.mse_loss(new_value, discounted_reward_i)\n",
        "\n",
        "        #train actor\n",
        "        _, _, dist = self.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",
        "\n",
        "        #train icm\n",
        "        cur_loss = cur_loss.mean() \n",
        "\n",
        "        #calculate combined loss:\n",
        "        overall_loss = SCALAR_BETA * (c_loss + a_loss) + cur_loss\n",
        "        overall_loss.backward(retain_graph=True)\n",
        "        self.optimizer.step()\n",
        "        \n",
        "        c_loss_batch.append(c_loss.detach().numpy())\n",
        "        a_loss_batch.append(a_loss.detach().numpy())      \n",
        "        icm_loss_batch.append(cur_loss.detach().numpy())\n",
        "\n",
        "\n",
        "    return np.array(c_loss_batch).mean(), np.array(a_loss_batch).mean(), np.array(icm_loss_batch).mean()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xHRtzJXocWKh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "outputId": "eb39e94f-3a58-4c53-fcd2-4f9f470f6e99"
      },
      "source": [
        "ENV = \"MountainCarContinuous-v0\" #\"MountainCarContinuous-v0\"  #Pendulum-v0 LunarLanderContinuous-v0\n",
        "\n",
        "env = gym.make(ENV)#Creating the Environment\n",
        "\n",
        "n_cpu = 6\n",
        "\n",
        "envs = SubprocVecEnv([lambda: gym.make(ENV) for i in range(n_cpu)])\n",
        "\n",
        "torch.manual_seed(42)\n",
        "torch.cuda.manual_seed(42)\n",
        "np.random.seed(42)\n",
        "#env.seed(42)\n",
        "\n",
        "\n",
        "\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 = True\n",
        "ENTROPY_BONUS = 0.00001\n",
        "LAMBDA = 0.95\n",
        "CLIP_GRAD = .1\n",
        "\n",
        "HIDDEN_SIZE = 96\n",
        "\n",
        "\n",
        "state_size  = env.observation_space.shape[0]\n",
        "action_size = env.action_space.shape[0]\n",
        "\n",
        "agent = Agent(state_size, action_size, hidden_size=256, extrinsic_reward=True, LR=1e-3)\n",
        "\n",
        "max_episodes = 300\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 = 1024\n",
        "\n",
        "# calc reshape stacking size\n",
        "shape = (max_steps*n_cpu, state_size)\n",
        "\n",
        "for ep in range(max_episodes+1):\n",
        "    states = envs.reset()\n",
        "\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",
        "        states = torch.from_numpy(states).float()\n",
        "        action, logprob, _  = agent.actor(states.to(device))  \n",
        "        value = agent.critic(states.to(device))\n",
        "        next_states, reward, done, _ = envs.step(action.cpu().numpy())\n",
        "        \n",
        "        state_batch.append(states)\n",
        "        next_state_batch.append(torch.from_numpy(next_states).float())\n",
        "        value_batch.append(value)\n",
        "        logprob_batch.append(logprob)\n",
        "        action_batch.append(action)\n",
        "        rewards_batch.append(torch.from_numpy(reward).float())  \n",
        "        masks.append(torch.from_numpy(1 - done).float())\n",
        "\n",
        "        states = next_states\n",
        "\n",
        "        if np.any(done):\n",
        "          states = envs.reset()\n",
        "    \n",
        "    # stack all gathered data\n",
        "    \n",
        "    state_batch = torch.stack(state_batch,dim=1).reshape(shape)\n",
        "    next_state_batch = torch.stack(next_state_batch,dim=1).reshape(shape)\n",
        "    actions_batch = torch.stack(action_batch, dim=1).reshape(max_steps*n_cpu,action_size)\n",
        "    logprob_batch = torch.stack(logprob_batch, dim=1).reshape(max_steps*n_cpu,action_size)    \n",
        "    \n",
        "    # Intrinsic Curiosity Calculation\n",
        "    forward_pred_err, inverse_pred_err = agent.ICM(state_batch, actions_batch, next_state_batch)\n",
        "    rewards = ((1. / ETA) * forward_pred_err).detach()\n",
        "    # rewards get out in the shape all rewards env1 all rewards env2 all rewards env3 ..   (1,X) \n",
        "  \n",
        "    intrinsic_rewards.append(rewards.mean().numpy())\n",
        "    if EXTRINSIC_REWARD == True:\n",
        "      rewards += torch.stack(rewards_batch, dim=1).reshape(-1).unsqueeze(1)\n",
        "\n",
        "    rewards_batch = rewards.reshape(n_cpu, max_steps) # reshapes into environment format  (num_env,X)\n",
        "    curiosity_loss = (1 - BETA) * inverse_pred_err + (BETA * forward_pred_err)\n",
        "\n",
        "    # calculate advantage:\n",
        "    next_value = agent.critic(torch.from_numpy(next_states).unsqueeze(0).float())#.item()\n",
        "    discounted_rewards, advantage = agent.compute_gae(next_value, rewards_batch, masks, value_batch)\n",
        "\n",
        "    # normalize advantage:\n",
        "    advantage = (advantage - advantage.mean()) / (advantage.std() + 1e-5)\n",
        "    \n",
        "    c_loss, a_loss, icm_loss = agent.ppo_update(states=state_batch, actions=actions_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 % 25 == 0:\n",
        "      test_rewards, test_entropy, test_steps = agent.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()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using:  cpu\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1374: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n",
            "  warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Episode: 300 | Ep_Reward: 27.66 | Average_100: -21.28"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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29xBXnlbZsaxUGfEikqeq6hoYO6KICaOHAzCvsgx3WLtdWfEi6aZkJpGB+fny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YL3FvIma9sP81DAPY4Z8SUfZL8VD9KaQzgUrCsjfjqWwlCZQryVd61bIf74qQR+78tP4oFnj/g9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\ntwrunHNwQzSNLtBrIrrTTfw7T5Vty/7kD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3EKhmKtbhnxboxpThS/HWe1vZQRPwiQEJgO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\n",
            "text/plain": [
              "<Figure size 1872x576 with 7 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jktbjKnMcWAA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "7b60d6ec-1572-43a1-bf02-05e3dbaebfaa"
      },
      "source": [
        "value_batch"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[tensor([[0.0974],\n",
              "         [0.1395],\n",
              "         [0.0766]], grad_fn=<AddmmBackward>), tensor([[0.0947],\n",
              "         [0.1397],\n",
              "         [0.0752]], grad_fn=<AddmmBackward>)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sHQOuzTo1DWa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c9d500e0-e932-47af-d647-fc7987c59481"
      },
      "source": [
        "action_size"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Js-6uEsqkMam",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "fb3fbe00-8577-43aa-ba5a-00fa959ff6d6"
      },
      "source": [
        "next_values"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[[0.1461],\n",
              "         [0.1344],\n",
              "         [0.1120]]], grad_fn=<AddBackward0>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4S5fr5xOkQn3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        },
        "outputId": "3f35c7fd-9fc8-42db-d9cc-87c54b59c222"
      },
      "source": [
        "torch.cat((torch.stack(value_batch, dim=1),next_values))"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-69-52f79fb0b960>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnext_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m: invalid argument 0: Sizes of tensors must match except in dimension 0. Got 2 and 3 in dimension 1 at /pytorch/aten/src/TH/generic/THTensor.cpp:711"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m2ZVjMUWzYxe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "0a5a1fa9-5bd2-42be-d4cc-a1d0aa18b749"
      },
      "source": [
        "torch.cat((torch.stack(value_batch, dim=1).squeeze(2), next_values.squeeze(0)),dim=1)"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.1452, 0.1460, 0.1461],\n",
              "        [0.1358, 0.1337, 0.1344],\n",
              "        [0.1136, 0.1131, 0.1120]], grad_fn=<CatBackward>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TVgP8TtgzqBg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "67579cfb-c7a4-4820-e7ed-fffb79172e79"
      },
      "source": [
        "list(zip(*rewards_batch))"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(-1.8561519361958574, 3.6333248840325156),\n",
              " (1.4210409069789238, 0.28636741209018735),\n",
              " (1.3552833391030732, 1.3479263853769237)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bX_l6a_IV2qM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2582e20a-db1b-4105-adc0-16565d66abf7"
      },
      "source": [
        "rewards_batch"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[tensor([-1.5731, -2.0929, -0.3506]), tensor([ 0.8438, -0.7112,  0.0590])]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2MdFTpIDW0xl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "rewards = torch.stack(rewards_batch, dim=1).reshape(-1).unsqueeze(1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F4pkuX7skOM8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "9cab4cbb-f406-49b3-86d9-f1f67f219c4d"
      },
      "source": [
        "rewards.reshape(n_cpu, max_steps)"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-1.5731,  0.8438],\n",
              "        [-2.0929, -0.7112],\n",
              "        [-0.3506,  0.0590]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    }
  ]
}