kW_Solution-Watch.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Watch a Trained Agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:unityagents:\n",
      "'Academy' started successfully!\n",
      "Unity Academy name: Academy\n",
      "        Number of Brains: 1\n",
      "        Number of External Brains : 1\n",
      "        Lesson number : 0\n",
      "        Reset Parameters :\n",
      "\t\t\n",
      "Unity brain name: BananaBrain\n",
      "        Number of Visual Observations (per agent): 0\n",
      "        Vector Observation space type: continuous\n",
      "        Vector Observation space size (per agent): 37\n",
      "        Number of stacked Vector Observation: 1\n",
      "        Vector Action space type: discrete\n",
      "        Vector Action space size (per agent): 4\n",
      "        Vector Action descriptions: , , , \n"
     ]
    }
   ],
   "source": [
    "from unityagents import UnityEnvironment\n",
    "import numpy as np\n",
    "env = UnityEnvironment(file_name=\"./Banana_Linux/Banana.x86_64\")\n",
    "\n",
    "# get the default brain\n",
    "brain_name = env.brain_names[0]\n",
    "brain = env.brains[brain_name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 12.0\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from dqn_agent import Agent\n",
    "\n",
    "agent = Agent(state_size=37, action_size=4, seed=0)\n",
    "agent.qnetwork_local.load_state_dict(torch.load('checkpoint.pth', map_location=lambda storage, loc: storage))\n",
    "\n",
    "env_info = env.reset(train_mode=False)[brain_name]\n",
    "\n",
    "score = 0\n",
    "state = env_info.vector_observations[0]\n",
    "while True:\n",
    "    action = agent.act(state)\n",
    "    env_info = env.step(action)[brain_name]\n",
    "    next_state = env_info.vector_observations[0]\n",
    "    reward = env_info.rewards[0]\n",
    "    done = env_info.local_done[0]\n",
    "    score += reward\n",
    "    state = next_state\n",
    "    if done:\n",
    "        break\n",
    "        \n",
    "print(\"Score: {}\".format(score))\n",
    "env.close()"
   ]
  }
 ],
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