5_actor_critic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a9bea4aa-07ec-4801-97e9-5f884feee7c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# act with actor critic on moon lander "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ec63c20d-cf37-4ea5-b578-edf2675e292a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import gymnasium as gym\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pdb\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.distributions import Categorical\n",
    "\n",
    "import sys\n",
    "import time\n",
    "from IPython.display import display, clear_output\n",
    "\n",
    "from utils import ReturnTracker, Agent\n",
    "from tqdm.auto import tqdm, trange\n",
    "\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "519c4fb8-1024-4801-88ae-685fa141b1fc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "class ActorCritic(nn.Module):\n",
    "    # attention, this implementation varies slightly from the one in our slides to achieve faster convergence!\n",
    "    def __init__(self, env, lr=0.02, gamma=0.99, device='cpu'):\n",
    "        super(ActorCritic, self).__init__()\n",
    "        self.embed = nn.Linear(8, 128).to(device)\n",
    "        self.action_log_probs = nn.Linear(128, 4).to(device)\n",
    "        self.values = nn.Linear(128, 1).to(device)\n",
    "        \n",
    "        self.reset_memory()\n",
    "        \n",
    "        self.total_num_steps = 0\n",
    "        self.env = env\n",
    "        self.lr = lr\n",
    "        self.gamma = gamma\n",
    "        self.optimizer = torch.optim.Adam(self.parameters(), lr=lr)\n",
    "        self.device = device\n",
    "\n",
    "    def forward(self, state):\n",
    "        state = torch.from_numpy(state).float().to(self.device)\n",
    "        state = F.relu(self.embed(state))\n",
    "        state_value = self.values(state)\n",
    "        \n",
    "        action_probs = F.softmax(self.action_log_probs(state), dim=-1)\n",
    "        action_distribution = Categorical(action_probs)\n",
    "        action = action_distribution.sample()\n",
    "        \n",
    "        self.logprobs_memory.append(action_distribution.log_prob(action))\n",
    "        self.values_memory.append(state_value)\n",
    "        \n",
    "        return action.item()\n",
    "    \n",
    "    def predict(self, state):\n",
    "        self.eval()\n",
    "        return self.forward(state)\n",
    "    \n",
    "    def calc_loss(self):\n",
    "        \n",
    "        # calculating discounted rewards:\n",
    "        returns = []\n",
    "        dis_reward = 0\n",
    "        for reward in self.rewards_memory[::-1]:\n",
    "            dis_reward = reward + self.gamma * dis_reward\n",
    "            returns.insert(0, dis_reward)\n",
    "                \n",
    "        # normalizing the returns:\n",
    "        returns = torch.tensor(returns).to(self.device)\n",
    "        returns = (returns - returns.mean()) / (returns.std())\n",
    "        \n",
    "        loss = 0\n",
    "        for logprob, value, return_ in zip(self.logprobs_memory, self.values_memory, returns):\n",
    "            advantage = return_  - value.item()\n",
    "            action_loss = -logprob * advantage\n",
    "            value_loss = F.smooth_l1_loss(value, return_.flatten())\n",
    "            loss += (action_loss + value_loss)   \n",
    "        return loss\n",
    "    \n",
    "    def learn(self, episodes, max_steps=None, tracker=None, verb=True):\n",
    "        \n",
    "        self.train()\n",
    "        \n",
    "        iterator = range(episodes)\n",
    "        if verb:\n",
    "            iterator = tqdm(iterator, leave=True)\n",
    "        \n",
    "        for episode in iterator:\n",
    "\n",
    "            if tracker is not None:\n",
    "                tracker.new_episode()\n",
    "            \n",
    "            state, info = self.env.reset()\n",
    "            \n",
    "            returns = 0\n",
    "            steps = 0\n",
    "            terminated = False\n",
    "            truncated = False\n",
    "            \n",
    "            while not terminated and not truncated:\n",
    "                \n",
    "                # pdb.set_trace()\n",
    "                action = self.forward(state)\n",
    "                \n",
    "                new_state, reward, terminated, truncated, info = self.env.step(action)\n",
    "                self.rewards_memory.append(reward)\n",
    "                    \n",
    "                state = new_state\n",
    "                self.total_num_steps += 1\n",
    "                steps += 1\n",
    "                returns += reward\n",
    "\n",
    "                if tracker is not None:\n",
    "                    tracker.add(reward)\n",
    "                \n",
    "                if max_steps is not None:\n",
    "                    if steps > max_steps:\n",
    "                        break\n",
    "\n",
    "            #update\n",
    "            self.optimizer.zero_grad()\n",
    "            loss = self.calc_loss()\n",
    "            loss.backward()\n",
    "            self.optimizer.step()        \n",
    "            self.reset_memory()\n",
    "                \n",
    "            if verb:\n",
    "                iterator.set_description(f\"total steps: {self.total_num_steps}, episode: {episode}, return: {returns:.4f}\")\n",
    "    \n",
    "    def reset_memory(self):\n",
    "        self.logprobs_memory = []\n",
    "        self.values_memory = []\n",
    "        self.rewards_memory = []\n",
    "        \n",
    "\n",
    "    def save(self, path):\n",
    "        if not os.path.exists(path):\n",
    "            os.makedirs(path)\n",
    "        torch.save(self.state_dict(), os.path.join(path, \"actor_critic.pt\"))\n",
    "\n",
    "    @classmethod\n",
    "    def load(\n",
    "            cls,\n",
    "            env,\n",
    "            path,\n",
    "            device=\"cpu\",\n",
    "            **kwargs,\n",
    "    ):\n",
    "        agent = cls(env, device=device, **kwargs)\n",
    "        agent.load_state_dict(\n",
    "            torch.load(os.path.join(path, \"actor_critic.pt\"), map_location=device)\n",
    "        )\n",
    "        return agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "50dc58e7-74d7-4a14-a55d-9f65a114d29a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "env = gym.make(\"LunarLander-v2\", continuous=False, render_mode='rgb_array').env\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9d90998e-3846-48a8-be4e-afbf4863839e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "state, info = env.reset()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c33a2dde-b2e6-4093-99e5-954325b4e3fb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "agent = ActorCritic(env)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f150d8af-d97f-4c5f-bbf8-50f592c4d863",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "tracker = ReturnTracker()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "08b37d5e-68a8-4314-88cc-bde030b79399",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d0067fc779534b56bf0cd6f83cc6f063",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "agent.learn(episodes=3000,\n",
    "            max_steps=1000, \n",
    "            tracker=tracker)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7f97b5d6-c29f-422b-b72e-a74036d14a6b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tracker.plot(smooth=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4b29429a-fbf7-433e-b2fd-f85b3de53605",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "steps: 91, returns: 42.01111070218411\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[15], line 28\u001b[0m\n\u001b[1;32m     26\u001b[0m plt\u001b[38;5;241m.\u001b[39mimshow(env\u001b[38;5;241m.\u001b[39mrender())\n\u001b[1;32m     27\u001b[0m plt\u001b[38;5;241m.\u001b[39maxis(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124moff\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 28\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     30\u001b[0m sys\u001b[38;5;241m.\u001b[39mstdout\u001b[38;5;241m.\u001b[39mflush()\n\u001b[1;32m     32\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m steps \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m300\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m returns \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m200\u001b[39m:\n",
      "File \u001b[0;32m~/.pyenv/versions/3.8.6/lib/python3.8/site-packages/matplotlib/pyplot.py:389\u001b[0m, in \u001b[0;36mshow\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    345\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    346\u001b[0m \u001b[38;5;124;03mDisplay all open figures.\u001b[39;00m\n\u001b[1;32m    347\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    386\u001b[0m \u001b[38;5;124;03mexplicitly there.\u001b[39;00m\n\u001b[1;32m    387\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    388\u001b[0m _warn_if_gui_out_of_main_thread()\n\u001b[0;32m--> 389\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_get_backend_mod\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.8.6/lib/python3.8/site-packages/matplotlib_inline/backend_inline.py:90\u001b[0m, in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m     88\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     89\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m figure_manager \u001b[38;5;129;01min\u001b[39;00m Gcf\u001b[38;5;241m.\u001b[39mget_all_fig_managers():\n\u001b[0;32m---> 90\u001b[0m         \u001b[43mdisplay\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     91\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfigure_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     92\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_fetch_figure_metadata\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfigure_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     93\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     94\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     95\u001b[0m     show\u001b[38;5;241m.\u001b[39m_to_draw \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m~/.pyenv/versions/3.8.6/lib/python3.8/site-packages/IPython/core/display_functions.py:298\u001b[0m, in \u001b[0;36mdisplay\u001b[0;34m(include, exclude, metadata, transient, display_id, raw, clear, *objs, **kwargs)\u001b[0m\n\u001b[1;32m    296\u001b[0m     publish_display_data(data\u001b[38;5;241m=\u001b[39mobj, metadata\u001b[38;5;241m=\u001b[39mmetadata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    297\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 298\u001b[0m     format_dict, md_dict \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minclude\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minclude\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexclude\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexclude\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    299\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m format_dict:\n\u001b[1;32m    300\u001b[0m         \u001b[38;5;66;03m# nothing to display (e.g. _ipython_display_ took over)\u001b[39;00m\n\u001b[1;32m    301\u001b[0m         \u001b[38;5;28;01mcontinue\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.8.6/lib/python3.8/site-packages/IPython/core/formatters.py:177\u001b[0m, in \u001b[0;36mDisplayFormatter.format\u001b[0;34m(self, obj, include, exclude)\u001b[0m\n\u001b[1;32m    175\u001b[0m md \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    176\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 177\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[43mformatter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    178\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[1;32m    179\u001b[0m     \u001b[38;5;66;03m# FIXME: log the exception\u001b[39;00m\n\u001b[1;32m    180\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m\n",
      "File \u001b[0;32m<decorator-gen-2>:2\u001b[0m, in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.8.6/lib/python3.8/site-packages/IPython/core/formatters.py:221\u001b[0m, in \u001b[0;36mcatch_format_error\u001b[0;34m(method, self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    219\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"show traceback on failed format call\"\"\"\u001b[39;00m\n\u001b[1;32m    220\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 221\u001b[0m     r \u001b[38;5;241m=\u001b[39m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    222\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m:\n\u001b[1;32m    223\u001b[0m     \u001b[38;5;66;03m# don't warn on NotImplementedErrors\u001b[39;00m\n\u001b[1;32m    224\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_return(\u001b[38;5;28;01mNone\u001b[39;00m, args[\u001b[38;5;241m0\u001b[39m])\n",
      "File \u001b[0;32m~/.pyenv/versions/3.8.6/lib/python3.8/site-packages/IPython/core/formatters.py:338\u001b[0m, in \u001b[0;36mBaseFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    336\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m    337\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 338\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mprinter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    339\u001b[0m \u001b[38;5;66;03m# Finally look for special method names\u001b[39;00m\n\u001b[1;32m    340\u001b[0m method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.8.6/lib/python3.8/site-packages/IPython/core/pylabtools.py:152\u001b[0m, in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_bases\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FigureCanvasBase\n\u001b[1;32m    150\u001b[0m     FigureCanvasBase(fig)\n\u001b[0;32m--> 152\u001b[0m \u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprint_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbytes_io\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    153\u001b[0m data \u001b[38;5;241m=\u001b[39m bytes_io\u001b[38;5;241m.\u001b[39mgetvalue()\n\u001b[1;32m    154\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fmt \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msvg\u001b[39m\u001b[38;5;124m'\u001b[39m:\n",
      "File \u001b[0;32m~/.pyenv/versions/3.8.6/lib/python3.8/site-packages/matplotlib/backend_bases.py:2295\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2289\u001b[0m     renderer \u001b[38;5;241m=\u001b[39m _get_renderer(\n\u001b[1;32m   2290\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure,\n\u001b[1;32m   2291\u001b[0m         functools\u001b[38;5;241m.\u001b[39mpartial(\n\u001b[1;32m   2292\u001b[0m             print_method, orientation\u001b[38;5;241m=\u001b[39morientation)\n\u001b[1;32m   2293\u001b[0m     )\n\u001b[1;32m   2294\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(renderer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_draw_disabled\u001b[39m\u001b[38;5;124m\"\u001b[39m, nullcontext)():\n\u001b[0;32m-> 2295\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2297\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches:\n\u001b[1;32m   2298\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtight\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "File \u001b[0;32m~/.pyenv/versions/3.8.6/lib/python3.8/site-packages/matplotlib/artist.py:73\u001b[0m, in \u001b[0;36m_finalize_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[1;32m     72\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdraw_wrapper\u001b[39m(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 73\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m renderer\u001b[38;5;241m.\u001b[39m_rasterizing:\n\u001b[1;32m     75\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n",
      "File \u001b[0;32m~/.pyenv/versions/3.8.6/lib/python3.8/site-packages/matplotlib/artist.py:50\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     47\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     48\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 50\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     52\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/.pyenv/versions/3.8.6/lib/python3.8/site-packages/matplotlib/figure.py:2837\u001b[0m, in \u001b[0;36mFigure.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   2834\u001b[0m         \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[1;32m   2836\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[0;32m-> 2837\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2838\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2840\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubfigs:\n\u001b[1;32m   2841\u001b[0m     sfig\u001b[38;5;241m.\u001b[39mdraw(renderer)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.8.6/lib/python3.8/site-packages/matplotlib/image.py:132\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m    131\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 132\u001b[0m         \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    133\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    134\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m    135\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m~/.pyenv/versions/3.8.6/lib/python3.8/site-packages/matplotlib/artist.py:50\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     47\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     48\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 50\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     52\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/.pyenv/versions/3.8.6/lib/python3.8/site-packages/matplotlib/axes/_base.py:3091\u001b[0m, in \u001b[0;36m_AxesBase.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3088\u001b[0m         a\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[1;32m   3089\u001b[0m     renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n\u001b[0;32m-> 3091\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3092\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3094\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maxes\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m   3095\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.8.6/lib/python3.8/site-packages/matplotlib/image.py:132\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m    131\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 132\u001b[0m         \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    133\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    134\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m    135\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m~/.pyenv/versions/3.8.6/lib/python3.8/site-packages/matplotlib/artist.py:50\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     47\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     48\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 50\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     52\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/.pyenv/versions/3.8.6/lib/python3.8/site-packages/matplotlib/image.py:646\u001b[0m, in \u001b[0;36m_ImageBase.draw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m    644\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mdraw_image(gc, l, b, im, trans)\n\u001b[1;32m    645\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 646\u001b[0m     im, l, b, trans \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmake_image\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    647\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_image_magnification\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    648\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m im \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    649\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mdraw_image(gc, l, b, im)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.8.6/lib/python3.8/site-packages/matplotlib/image.py:956\u001b[0m, in \u001b[0;36mAxesImage.make_image\u001b[0;34m(self, renderer, magnification, unsampled)\u001b[0m\n\u001b[1;32m    953\u001b[0m transformed_bbox \u001b[38;5;241m=\u001b[39m TransformedBbox(bbox, trans)\n\u001b[1;32m    954\u001b[0m clip \u001b[38;5;241m=\u001b[39m ((\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_clip_box() \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes\u001b[38;5;241m.\u001b[39mbbox) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_clip_on()\n\u001b[1;32m    955\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure\u001b[38;5;241m.\u001b[39mbbox)\n\u001b[0;32m--> 956\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_image\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_A\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbbox\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtransformed_bbox\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclip\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    957\u001b[0m \u001b[43m                        \u001b[49m\u001b[43mmagnification\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munsampled\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43munsampled\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.8.6/lib/python3.8/site-packages/matplotlib/image.py:557\u001b[0m, in \u001b[0;36m_ImageBase._make_image\u001b[0;34m(self, A, in_bbox, out_bbox, clip_bbox, magnification, unsampled, round_to_pixel_border)\u001b[0m\n\u001b[1;32m    554\u001b[0m     alpha \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_scalar_alpha()\n\u001b[1;32m    555\u001b[0m     output_alpha \u001b[38;5;241m=\u001b[39m _resample(  \u001b[38;5;66;03m# resample alpha channel\u001b[39;00m\n\u001b[1;32m    556\u001b[0m         \u001b[38;5;28mself\u001b[39m, A[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m, \u001b[38;5;241m3\u001b[39m], out_shape, t, alpha\u001b[38;5;241m=\u001b[39malpha)\n\u001b[0;32m--> 557\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[43m_resample\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# resample rgb channels\u001b[39;49;00m\n\u001b[1;32m    558\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_rgb_to_rgba\u001b[49m\u001b[43m(\u001b[49m\u001b[43mA\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout_shape\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43malpha\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    559\u001b[0m     output[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m, \u001b[38;5;241m3\u001b[39m] \u001b[38;5;241m=\u001b[39m output_alpha  \u001b[38;5;66;03m# recombine rgb and alpha\u001b[39;00m\n\u001b[1;32m    561\u001b[0m \u001b[38;5;66;03m# at this point output is either a 2D array of normed data\u001b[39;00m\n\u001b[1;32m    562\u001b[0m \u001b[38;5;66;03m# (of int or float)\u001b[39;00m\n\u001b[1;32m    563\u001b[0m \u001b[38;5;66;03m# or an RGBA array of re-sampled input\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.8.6/lib/python3.8/site-packages/matplotlib/image.py:193\u001b[0m, in \u001b[0;36m_resample\u001b[0;34m(image_obj, data, out_shape, transform, resample, alpha)\u001b[0m\n\u001b[1;32m    191\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m resample \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    192\u001b[0m     resample \u001b[38;5;241m=\u001b[39m image_obj\u001b[38;5;241m.\u001b[39mget_resample()\n\u001b[0;32m--> 193\u001b[0m \u001b[43m_image\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresample\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtransform\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    194\u001b[0m \u001b[43m                \u001b[49m\u001b[43m_interpd_\u001b[49m\u001b[43m[\u001b[49m\u001b[43minterpolation\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    195\u001b[0m \u001b[43m                \u001b[49m\u001b[43mresample\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    196\u001b[0m \u001b[43m                \u001b[49m\u001b[43malpha\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    197\u001b[0m \u001b[43m                \u001b[49m\u001b[43mimage_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_filternorm\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    198\u001b[0m \u001b[43m                \u001b[49m\u001b[43mimage_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_filterrad\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    199\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# test\n",
    "\n",
    "while True:\n",
    "    \n",
    "    terminated, truncated = False, False\n",
    "    \n",
    "    state, info = env.reset()\n",
    "    \n",
    "    steps = 0\n",
    "    returns = 0\n",
    "    \n",
    "    while not terminated and not truncated:\n",
    "\n",
    "        time.sleep(.05)\n",
    "        clear_output(wait=True)\n",
    "\n",
    "        action = agent.predict(state)\n",
    "        new_state, reward, terminated, truncated, info = env.step(action)\n",
    "\n",
    "        steps += 1\n",
    "        state = new_state\n",
    "        returns += reward\n",
    "        \n",
    "        print('steps: {}, returns: {}'.format(steps, returns))\n",
    "\n",
    "        plt.imshow(env.render())\n",
    "        plt.axis('off')\n",
    "        plt.show()\n",
    "    \n",
    "        sys.stdout.flush()\n",
    "        \n",
    "        if steps > 300 or returns > 200:\n",
    "            break\n",
    "    \n",
    "    time.sleep(1.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "af328d06-b27c-4342-93ef-c394167338c0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "agent.save('models')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fd76fb80-b15f-4de1-9d78-817114e3b635",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "agent = ActorCritic.load(env, 'models')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b6b2b3a-2bbb-49d7-9bb4-8efd28bb30bc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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