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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Navigation\n",
"\n",
"---\n",
"\n",
"You are welcome to use this coding environment to train your agent for the project. Follow the instructions below to get started!\n",
"\n",
"### 1. Start the Environment\n",
"\n",
"Run the next code cell to install a few packages. This line will take a few minutes to run!"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[31mtensorflow 1.7.1 has requirement numpy>=1.13.3, but you'll have numpy 1.12.1 which is incompatible.\u001b[0m\r\n",
"\u001b[31mipython 6.5.0 has requirement prompt-toolkit<2.0.0,>=1.0.15, but you'll have prompt-toolkit 3.0.18 which is incompatible.\u001b[0m\r\n"
]
}
],
"source": [
"!pip -q install ./python"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"import random\n",
"import torch\n",
"from collections import deque\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"from dqn_agent import Agent"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The environment is already saved in the Workspace and can be accessed at the file path provided below. Please run the next code cell without making any changes."
]
},
{
"cell_type": "code",
"execution_count": 3,
"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",
"\n",
"# please do not modify the line below\n",
"env = UnityEnvironment(file_name=\"/data/Banana_Linux_NoVis/Banana.x86_64\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Environments contain **_brains_** which are responsible for deciding the actions of their associated agents. Here we check for the first brain available, and set it as the default brain we will be controlling from Python."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# get the default brain\n",
"brain_name = env.brain_names[0] # select first brain\n",
"brain = env.brains[brain_name] # set the brain that will be controlled from python"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2. Examine the State and Action Spaces\n",
"\n",
"Run the code cell below to print some information about the environment."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of agents: 1\n",
"Number of actions: 4\n",
"States look like: [ 1. 0. 0. 0. 0.84408134 0. 0.\n",
" 1. 0. 0.0748472 0. 1. 0. 0.\n",
" 0.25755 1. 0. 0. 0. 0.74177343\n",
" 0. 1. 0. 0. 0.25854847 0. 0.\n",
" 1. 0. 0.09355672 0. 1. 0. 0.\n",
" 0.31969345 0. 0. ]\n",
"States have length: 37\n"
]
}
],
"source": [
"# reset the environment\n",
"env_info = env.reset(train_mode=True)[brain_name]\n",
"\n",
"# number of agents in the environment\n",
"print('Number of agents:', len(env_info.agents))\n",
"\n",
"# number of actions\n",
"action_size = brain.vector_action_space_size\n",
"print('Number of actions:', action_size)\n",
"\n",
"# examine the state space \n",
"state = env_info.vector_observations[0]\n",
"print('States look like:', state)\n",
"state_size = len(state)\n",
"print('States have length:', state_size)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3. Take Random Actions in the Environment\n",
"\n",
"In the next code cell, you will learn how to use the Python API to control the agent and receive feedback from the environment.\n",
"\n",
"Note that **in this coding environment, you will not be able to watch the agent while it is training**, and you should set `train_mode=True` to restart the environment."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Score: 0.0\n"
]
}
],
"source": [
"env_info = env.reset(train_mode=True)[brain_name] # reset the environment\n",
"state = env_info.vector_observations[0] # get the current state\n",
"score = 0 # initialize the score\n",
"while True:\n",
" action = np.random.randint(action_size) # select an action\n",
" env_info = env.step(action)[brain_name] # send the action to the environment\n",
" next_state = env_info.vector_observations[0] # get the next state\n",
" reward = env_info.rewards[0] # get the reward\n",
" done = env_info.local_done[0] # see if episode has finished\n",
" score += reward # update the score\n",
" state = next_state # roll over the state to next time step\n",
" if done: # exit loop if episode finished\n",
" break\n",
" \n",
"print(\"Score: {}\".format(score))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When finished, you can close the environment."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"#env.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4. It's Your Turn!\n",
"\n",
"Now it's your turn to train your own agent to solve the environment! A few **important notes**:\n",
"- When training the environment, set `train_mode=True`, so that the line for resetting the environment looks like the following:\n",
"```python\n",
"env_info = env.reset(train_mode=True)[brain_name]\n",
"```\n",
"- To structure your work, you're welcome to work directly in this Jupyter notebook, or you might like to start over with a new file! You can see the list of files in the workspace by clicking on **_Jupyter_** in the top left corner of the notebook.\n",
"- In this coding environment, you will not be able to watch the agent while it is training. However, **_after training the agent_**, you can download the saved model weights to watch the agent on your own machine! "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def dqn(n_episodes=1000, max_t=1000, eps_start=1.0, eps_end=0.01, eps_decay=0.995, train_mode=True):\n",
" \"\"\"Deep Q-Learning.\n",
" \n",
" Params\n",
" ======\n",
" n_episodes (int): maximum number of training episodes\n",
" max_t (int): maximum number of timesteps per episode\n",
" eps_start (float): starting value of epsilon, for epsilon-greedy action selection\n",
" eps_end (float): minimum value of epsilon\n",
" eps_decay (float): multiplicative factor (per episode) for decreasing epsilon\n",
" train_mode (bool): if \"True\" set env to training mode\n",
" \"\"\"\n",
" scores = [] # list containing scores from each episode\n",
" scores_mean = [] # list mean score\n",
" scores_window = deque(maxlen=100) # last 100 scores\n",
" eps = eps_start # initialize epsilon\n",
" \n",
" for i_episode in range(1, n_episodes+1):\n",
" env_info = env.reset(train_mode=True)[brain_name] # reset env\n",
" state = env_info.vector_observations[0] # get current state\n",
" score = 0\n",
" for t in range(max_t):\n",
" action = agent.act(state, eps) # choose an action\n",
" env_info = env.step(action)[brain_name] # send action to env\n",
" next_state = env_info.vector_observations[0] # get next state from env\n",
" reward = env_info.rewards[0] # get reward from env\n",
" done = env_info.local_done[0] # check if episode is done\n",
" \n",
" agent.step(state, action, reward, next_state, done) # step\n",
" state = next_state\n",
" score += reward\n",
" if done:\n",
" break \n",
" scores_window.append(score) # save most recent score\n",
" scores.append(score) # save most recent score\n",
" scores_mean.append(np.mean(scores_window))\n",
" \n",
" eps = max(eps_end, eps_decay*eps) # decrease epsilon\n",
" \n",
" print('\\rEpisode {}\\tAverage Score: {:.2f}'.format(i_episode, np.mean(scores_window)), end=\"\")\n",
" \n",
" if i_episode % 100 == 0:\n",
" print('\\rEpisode {}\\tAverage Score: {:.2f}'.format(i_episode, np.mean(scores_window)))\n",
" if np.mean(scores_window)>=13.0:\n",
" print('\\nEnvironment solved in {:d} episodes!\\tAverage Score: {:.2f}'.format(i_episode-100, np.mean(scores_window)))\n",
" torch.save(agent.qnetwork_local.state_dict(), 'checkpoint.pth')\n",
" break\n",
" return scores, scores_mean"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Episode 100\tAverage Score: 1.15\n",
"Episode 200\tAverage Score: 4.48\n",
"Episode 300\tAverage Score: 6.94\n",
"Episode 400\tAverage Score: 9.54\n",
"Episode 500\tAverage Score: 12.80\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1ef5ce7588>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"agent = Agent(state_size=state_size, action_size=action_size, seed=0)\n",
"scores, mean = dqn(n_episodes=500, eps_decay=0.995, eps_end=0.01)\n",
"\n",
"# plot the scores\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111)\n",
"plt.plot(np.arange(len(scores)), scores, label='Score')\n",
"plt.plot(np.arange(len(mean)), mean, label='Mean')\n",
"plt.ylabel('Score')\n",
"plt.xlabel('Episode #')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Episode 100\tAverage Score: 4.19\n",
"Episode 200\tAverage Score: 9.17\n",
"Episode 299\tAverage Score: 13.04\n",
"Environment solved in 199 episodes!\tAverage Score: 13.04\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1ef5ce74a8>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"agent = Agent(state_size=state_size, action_size=action_size, seed=0)\n",
"scores, mean = dqn(n_episodes=500, eps_decay=0.975, eps_end=0.02)\n",
"\n",
"# plot the scores\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111)\n",
"plt.plot(np.arange(len(scores)), scores, label='Score')\n",
"plt.plot(np.arange(len(mean)), mean, label='Mean')\n",
"plt.ylabel('Score')\n",
"plt.xlabel('Episode #')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Episode 100\tAverage Score: 3.11\n",
"Episode 200\tAverage Score: 8.57\n",
"Episode 300\tAverage Score: 11.61\n",
"Episode 335\tAverage Score: 13.06\n",
"Environment solved in 235 episodes!\tAverage Score: 13.06\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1e9f1636d8>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"agent = Agent(state_size=state_size, action_size=action_size, seed=0)\n",
"scores, mean = dqn(n_episodes=500, eps_decay=0.98, eps_end=0.02)\n",
"\n",
"# plot the scores\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111)\n",
"plt.plot(np.arange(len(scores)), scores, label='Score')\n",
"plt.plot(np.arange(len(mean)), mean, label='Mean')\n",
"plt.ylabel('Score')\n",
"plt.xlabel('Episode #')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Test the agent"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Episode 10\tAverage Score: 11.80"
]
},
{
"data": {
"image/png": 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x7Ja36x5XlBWyvKwwqLrH/6zbz992HOQ/r18Ulv7coTBS+K5r6LSEMQk72nr46iP1vLrrIJUlHn5z21IWBT7TiZQV5ZKanETN/sNxceV+MmFLGCJyP3AN0KaqiwLHvg58DGgPnPZlVV01znNXAD8AXMAvVfVb4YozUTxS3YgnM4VL5xc4HUpEja17bG7u5pnN/uRxz5qt3LNm67G6xxULCjln5jvrHq1dffznk5tZOnsKHwxTf+5QmJqdRrEng1orfJ+SvsFhfvL8Dn7+wk4yUlx884ZF3PKu0qBa66YmJ7FwRi7V+w5FIFLnhfMK4zfAj4HfjTn+PVX9zkRPEhEX8BNgGdAAvCkij6nqpnAFGu96+od4amML7z/bS2py4q7VFBHKZuRSNuPkdY8rFhRy0bxpZKS4ItKfO1T8K76t8B2s0Wsqbqwq5s6rF5zycGVVSR5/fGMvg8M+UhzcaicSwtnT+0URmTWJpy4FdgR6eyMifwKuAyxhTNJT9S30Dfq48ez4H446FcHUPRZ7PbyxpyMi/blDodzrZnV9C51HB3Fnxl+tKlTGrqn448fO5fwzJ7e9S2Wph/v/tputLd0nHcKKdU7UMD4lIh8G1gFfUNWx13LFwP5R9xuAcyd6MRG5HbgdoLQ0eocLnLSyupHSKZmObL0dKyaqezyzuZWls6dEpD93KFQUewCob+p0dH+raHWiNRWTVVXi/8yr9x+2hBFiPwO+AWjg93eBfxhzznjX/BNuJKSq9wL3AixZsiS6NhyKAq1dffxt5wE+fdncqJzZE41G1z2iaXFeMEav+LaEcbzRayoumZ/P3dcuonTq6S9g9eZlMC07lZp9h7n1vJkhiDR6RTRhqGrryG0RuQ94YpzTGoCSUfe9QFOYQ4tbj9Y0ogo3JMDsKOPfMn7m1EzqGq2OMWLsmoqffehsVoyzpmKyRITKEg/V++O/8B3RhCEiRaraHLh7A1A/zmlvAnNFZDbQCNwMfDBCIcadldVNVJZ4YmL83YRGebGbmv2WMMZbU/HPy+eRHYaWxFWleTyzuS3ua0dhK+mLyIPAq8B8EWkQkY8C94hInYjUApcCnw+cO0NEVgGo6hDwKeApYDPwP6q6MVxxxrMtLV1sbu6yq4sEU+F103Col44jA06H4pgdbT188L7X+fyfN+DNy+SxT13I195XFpZkAf4tQgBq4nyGWjhnSd0yzuFfTXBuE3D1qPurgHeszzCnZmV1I8lJwjUV8b+gyLytPFD4rmvs5OJ5+Q5HE1mns6bidFR43YhAzb7Dcf2Z20rvODXsUx6tbuLieflMjbJN8kx4jTTGqmuI7y+vsUKxpmKyctJTmFuQHfd1DEsYcer1XQdp6erjK+91vhuciayc9BTOyM9KmB7foVxTcTqqSvJ4alMLqhq3MxItYcSpldWNZKcls6ys0OlQjAMqit28vrvD6TDCKhxrKk5HZamHP6/bz56DR+N2kokljDjUOzDM6voWrlo0nfQUZ1uZGmeUez08UtNEW3cfBTnpTocTcuFaU3E6qkoDhe/9h+I2YcT3xicJ6pnNrfT0D3GDbQWSsCq8/gV89XG4EeHGpk7e/7NXOHikn5996Gx+/X/f5XiyAJhbkENWqovqOO7AZwkjDq2sbqTInc55s22L60RVVpRLksCG/fGXMB6tacKVJKz+7EVcVV4UNfUCV5JQ4fXE9RoYSxhx5mBPPy9sa+e6yuKo31nVhE9WWjJzCrLjrse3qrK6vpkL5kxjSlaq0+G8Q2Wph01NXfQNDjsdSlhYwogzj29oYtintljPUF7sobahE9X42WJtY1MX+zt6uWrRdKdDGVdViYchn7KxKb4S9QhLGHFmZU0TZUW5zJ+e43QoxmEVXjcHevpp6epzOpSQWVPfgitJWFYWnQmjMlD4jtc6hiWMOLKzvYcN+w/b1YUB/L0xgLhZj6GqrKpv5tzZU6JyOAqgICedYk8G1XFax7CEEUcerW4kSeDayhlOh2KiQFlRLq4koS5OEsb2th52tR+J2uGoEZWlHmrsCsNEM1VlZU0jF8yZRmFu/M27N6cuPcXFvMKcuOnxvbquBRG4cmF0J4yqEg+Nh3tp646focARljDixFt7D7G/o5frK204yrytotjf4zseCt+r65tZMjOPgij/g+jYAr44vMqwhBEnVlY3kpHiYkWUX66byCr3ujl0dJCGQ71Oh3Jadh84wpaWblYsiv6dlxfOcJPikrisY1jCiAP9Q8M8UdvM8oWFZIVpv38Tm0ZWfMf6eozV9f6+a7HwB1F6iosFRbl2hWGi01+3ttPZO2izo8w7zJ+eQ4pLYn6m1Jr6FhaXeCj2ZDgdSlAqSzzUNhxm2Bf7Q4GjWcKIAyvXNzItO40L50R+S2cT3dKSXZw1PTeme3w3HDpKbUNn1M+OGq2q1MORgWG2t3U7HUpIhbNF6/0i0iYi9aOO/ZeIbBGRWhFZKSKeCZ67J9DKtUZE1oUrxnjQeXSQ57a0ce3iGSS7LP+bdyr3umN6xfea+haAmEoYlSV5QPwt4AvnN8xvgBVjjj0NLFLVCmAbcOcJnn+pqlaq6pIwxRcXnqxrZmDYZ8NRZkIVxW66+4bYe/Co06FMypr6FhYU5TJzauxsGT5raiaezJS4q2OELWGo6otAx5hja1V1KHD3NcAbrvdPFI9UNzKnIPtYW05jxjq24jsGC9+tXX2s23sopq4uAESEyhJP3LVsdXIM4x+A1RM8psBaEXlLRG4/0YuIyO0isk5E1rW3t4c8yGi2v+Mob+zp4Iaq4qjZ4tlEn3mFOaQmJ1HXEHt/7T610T8cdXV5bCUM8Lds3d7WQ3ffoNOhhIwjCUNEvgIMAQ9McMoFqno2cBVwh4hcNNFrqeq9qrpEVZfk5ydOw3uAR2saAbjOtgIxJ5DiSqKsKDcmZ0qtrmthTkE2cwpibzPNylIPqvGzlxc4kDBE5CPANcCHdIIqnKo2BX63ASuBpZGLMDaoKg9XN7J09hS8ec53GzPRrcLrpr6xE18MTfM82NPP67sPxtxw1IhK70jL1ti7sptIRBOGiKwA/g24VlXHrcCJSJaI5IzcBpYD9eOdm8jqGjvZ1X7Eit0mKOXFbo4MDLPrwBGnQwna05ta8WlsLNYbjzszhTPys6jeFz91jHBOq30QeBWYLyINIvJR4MdADvB0YMrszwPnzhCRVYGnFgIvi8gG4A3gSVVdE644Y9XK6kZSXUlcXR79WyUY51UE/tqNpfUYq+pbmDk1k7Ki2J3QUVWSR83++NjLCyBs+0io6i3jHP7VBOc2AVcHbu8CFocrrngwNOzj8Q1NXL6gAHdGitPhmBhwZn4WGSkuahs6uaEq+icndh4d5JUdB/joe2bH9ISOylIPD61voOFQLyVTYn/o2FZ6xaCXdhzgQM+ADUeZoCW7klg4IzdmemM8s7mVIZ9yVQxsNngiVSWBDnxxUsewhBGDVq5vxJOZwiXzC5wOxcSQcq+bjU1dDA37nA7lpFbXtzDDnc7iwBqSWHXW9BzSU5Lipo5hCSPG9PQPsXZTC9dUFJGabP/3meBVeN30Dg6zsz26C989/UO8uL2dKxdNj+nhKPBf2VUUe+JmplTQ3zgicqGI3Ba4nS8is8MXlpnImvoW+gZtKxBz6sqL/cMjtVG+gO+5LW0MDPniZkJHZamHjY1d9A8NOx3KaQsqYYjIXfinw47s/ZQC/CFcQZmJPVLdSOmUTM4uzXM6FBNjzpiWRVaqK+p7Y6ypbyY/J41z4uS/8aoSDwPDPjY3x/7OtcFeYdwAXAscgWOzmmJv6WWMa+ns4287D3C9bQViJiEpSVhU7I7qlce9A8M8v6WdKxcWkpQUH/+NVx5r2Rr7dYxgE8ZAYFW2wrEFdSbCHtvQiCo2HGUmrcLrZlNzF4NRWvh+YVs7vYPDMT87arQidwbTc9PjYqZUsAnjf0TkF4BHRD4GPAPcF76wzHgeXt9IZYmH2dMsX5vJKfd6GBjysa01OodHVtc3k5eZwrmzpzgdSkhVlsRH4TuohKGq3wH+AjwEzAe+pqo/Cmdg5nibm7vY0tJtVxfmtFQUB3p8R+GwVP/QMM9tbmN52fS4awZWVeph78GjHOzpdzqU03LSld4i4gKeUtUr8DdAMg54pLqR5CThmor4uVQ3kTdzaiY56cnUNnZys9PBjPG3HQfo7h9iRQxuZX4ylYEFfBsaDnPZWYUORzN5J03jqjoMHBWR2F5BE8OGfcqjNU1cPC+fqdlpTodjYpiIUOF1R+UVxuq6FnLSk7ngzPjrTV/udeNKkphv2RrsXlJ9QJ2IPE1gphSAqn4mLFGZ47y+6yAtXX38+zULnA7FxIHyYg+/enkX/UPDpCW7nA4HgMFhH2s3tXLFgsK4XJCamZrM/MKcmK9jBJswngz8GAc8XN1ITloyVyyI3UtZEz0qvG4Gh5WtLd3HdrF12mu7DtLZOxizvS+CUVnq4fGaJnw+jdkpw8EWvX8LPAi8Ffj5Y+CYCbPegWHW1LdwVfl00lOi469BE9vKA4XvaFqPsbq+hcxUFxfNi9+umVUlHrr7h9h1oMfpUCYt2JXelwDbgZ8APwW2nahtqgmdpze30tM/xPU2O8qEiDcvg7zMlKipYwz7lLUbW7j0rIK4/qOoKrCAb30M1zGCHSz8LrBcVS9W1YuAK4HvhS8sM+KR6kaK3OmcN3uq06GYOCEilHs91EbJFiHr9nRwoGcgroejAM6Ylk1OenJM1zGCTRgpqrp15I6qbsO/n5QJowM9/bywrZ3rKotjdszTRKfFXjfbWrvpG3R+Q7zV9S2kJSdxaZxv15+UJFSWeGJ6plSwCWOdiPxKRC4J/NyHv5ZxQiJyv4i0iUj9qGNTRORpEdke+D3uDmMi8pHAOdtF5CNBxhlXntjQxLBPufFsG44yoVVe7GbYp2xq7nI0Dp9PWVPfwsXz8slKC1sD0KhRVeJha0sXRweGnA5lUoJNGJ8ANgKfAT4LbAI+HsTzfgOsGHPsS8CzqjoXeDZw/zgiMgW4CzgXWArcNVFiiWcrqxspK8plXqHt82hC61iPb4frGDUNh2np6uOqOFysN57KUg8+ja4JB6ci2ISRDPxAVW9U1RuAHwInrU6p6otAx5jD1wEjM6x+C1w/zlOvBJ5W1Q5VPYR/hfnYxBPXdrb3sKGh07YCMWFRmJtGfk6a419ca+pbSHFJTK9+PhWVJf6/e2O1jhFswngWyBh1PwP/BoSTUaiqzQCB3+MNXBYD+0fdbwgcewcRuV1E1onIuvb29kmGFH0eqW4kSeDayhlOh2LikIhQUeymrtG5Ly5VZXV9MxfMmYY7IzFKolOyUpk5NTNmW7YGmzDSVfXY5OHA7czwhATAeBVeHe9EVb1XVZeo6pL8/PiYw62qrKxu5II50yjMTXc6HBOnyr1udrT1cKTfmfH0jU1d7O/ojfvZUWNVBQrf/o4RsSXYhHFERM4euSMiS4DeSb5nq4gUBV6nCGgb55wGoGTUfS/QNMn3izlv7T1Ew6FeG44yYVXhdeNTHCt8r65vxpUkLCtLrIRRWeKhrbuf5s4+p0M5ZcEmjM8B/ysiL4nIi8CfgE9N8j0fA0ZmPX0EeHScc54ClotIXqDYvTxwLCE8XN1IRoqLKxcm1j8kE1mLHFzx7R+OauG8M6YwJSs14u/vpKrS2K1jnDBhiMi7RGS6qr4JnAX8GRgC1gC7T/biIvIg8CowX0QaROSjwLeAZSKyHVgWuI+ILBGRXwKoagfwDeDNwM/dgWNxr39omCdrm7lyYWFCTDM0zinISafInU5dQ+S/uLa39bCr/Qgr4qizXrAWFOWSmpwUkwnjZN9IvwCuCNx+N/Bl4NNAJXAv8IETPVlVb5ngocvHOXcd8I+j7t8P3H+S+OLO81va6ewdtK1ATESUF7sdWfG9qq4ZEbhyYWLMjhotNTmJRTNyY7LwfbIhKdeov+xvAu5V1YdU9avAnPCGlpgeqW5kWnYaF86Jv54AJvpUeN3saj9Cd99gRN93TX0L75o5hYKcxJzUUVmSR11jZ9T2Vp/ISROGiIxchVwOPDfqMRsvCbHOo4M8t6WNaxfPiLsWlSY6lQcW8NU3Rq7wvfvAEba0dLMiwWbA8uLFAAAVAElEQVRHjVZV6qFv0MfWlujsrT6Rk30rPQi8ICKP4p8V9RKAiMwBYnOpYhR7sq6ZgWGfbQViImZkq/NIrsdYXd8MkNAJY6Rla3WM1TFOmDBU9ZvAF/Bv8XGhvj1xOAl/LcOE0MrqBuYUZLNwRq7ToZgEMSUrFW9eRkRnSq2pb2FxiYcZnoyTnxynvHkZTMtOi7k6RjA9vV9T1ZWqOro16zZVXR/e0BLLm3s6eHPPIW6oKkbEdqY1kVPhdVMXocL3/o6j1DZ0JtxivbFE/DvXxtpMKRsojwJNh3v5xB/eYtbUTP7+vJlOh2MSTHmxh70Hj9J5NPyF76c2tgAkfMIAfx1jV/uRiHzuoWIJw2G9A8Pc/vt19A36uO/DSxJmTx0TPSq8I3WM8F9lrK5voawol5lTs8L+XtGuKlDHqHFgHcxkWcJwkKrypYdr2djUxfdvqmSubWNuHLBoRmDFd5gL361dfby195BdXQSUe92IEFN1DEsYDrr3xV08WtPEF5bN44qyxFvAZKKDOzOFWVMzw94b49hwVIL0vjiZnPQU5hXkxFQdwxKGQ/66tY1vrdnCe8uLuONSWwNpnFXu9YR9ptSqumbmFmQzp8CupEeMFL5jZedaSxgO2NXew6cfrOas6bn81/+psFlRxnEVxW4aD/dysKc/LK9/sKefN3Z32HDUGFWlHg4fHWTPwaNOhxIUSxgR1tU3yMd+t44UVxL33noOmam2YN44rzzMhe+1m1rxKQm52eCJVJYGFvDFSB3DEkYEDfuUz/2phr0Hj/LTD51NyZRw9qAyJngLZ+QiEr4e36vrW5g5NZMFRTYcNdrcghyyUl0xU8ewhBFB//30Vp7b0sZd7yvjvDOmOh2OMcfkpKdwxrSssOxc23l0kFd2HGDFouk2/DqGK0mo8Po78MUCSxgR8viGJn7y/E5uWVpii/NMVKrwesJyhfH05laGfMrVNhw1rqpSD5ubu+gbHHY6lJOyhBEBG5s6+de/bGDJzDz+49pF9leWiUrlxW5auvpo6wpt69A19c0UezKOLRA0x6ss8TDkU+od6EtyqixhhNnBnn5u/91b5GWm8rO/P4fUZPvITXQKx4rvnv4hXtx+gCsX2nDUREYK37FQx4j4t5eIzBeRmlE/XSLyuTHnXCIinaPO+Vqk4wyFwWEfn3hgPQd6+vnFreeQn5PmdEjGTKhsRi5JEtoe389taWNgyGeL9U6gICedYk9GTNQxIj6nU1W34m/xioi4gEZg5TinvqSq10QytlC7+/FNvLG7g+/fVElFoFGNMdEqMzWZuQU5Ib3CWF3XTH5OGueU5oXsNeNRVWlsFL6dHh+5HNipqnsdjiPk/vj6Pn7/2l7+6aIzrD+3iRnlXje1DZ0hWXncOzDMX7e2s2LhdJKSbDjqRCpLPDQe7g15/SjUnE4YN+Pv6jeed4vIBhFZLSILJ3oBEbldRNaJyLr29vbwRHmK3tzTwV2P1XPxvHy+uOIsp8MxJmgVXjcHevppCcEX1wvb2ugdHLbV3UGoClyBRXsHPscShoikAtcC/zvOw+uBmaq6GPgR8MhEr6Oq96rqElVdkp+fH55gT8FIbwtvXiY/vLkKl/1lZWLISMvWUNQxVte3kJeZwtLZU077teLdwhm5pLgk6gvfTl5hXAWsV9XWsQ+oapeq9gRurwJSRGRapAM8Vcf3tjgHd6b1tjCxZUFRLslJctrrMfqHhnl2cxvLy6aT7HJ6ICP6pae4KCvKjfotQpz8f/IWJhiOEpHpEpiDJyJL8cd5MIKxnbKxvS1sR04Ti9JTXMwrzDntFd8vbz9AT/+QzY46BZUl/h2Dh33Ru3OtIwlDRDKBZcDDo459XEQ+Hrj7AaBeRDYAPwRu1ijf/3ekt8W/LJ9vvS1MTKvwuqlrOL0tt1fXt5CTnsz5Z0b9wEDUqCrN4+jAMNtau50OZUKOJAxVPaqqU1W1c9Sxn6vqzwO3f6yqC1V1saqep6qvOBFnsEb3tvjkJWc6HY4xp6Xc6+bQ0UEaDvVO6vmDwz6e3tTKsgWFtlD1FFSWRP8CPvt/8zRZbwsTbyqK/V9cky18v7brIJ29g6yw2VGnZObUTPIyU6K6jmEJ4zRYbwsTj+ZNzybVlTTpHt+r6lrITHVx0TznZy3GEhE51oEvWlnCmCTrbWHiVVqyi7OKciY1U2rYpzy9qYXLziogPcUVhujiW2VJHtvbeujuG3Q6lHFZwpik764N9La4dqH1tjBxp7zYTV1jJ75TnLHz5p4ODvQMcJVtZT4pVaUeVEO7n1coWcKYhMc3NPHTv+7klqWl/P25pU6HY0zIVXjddPcNsbfj1HpNr6lvIS05iUvm23DUZCwuie6WrZYwTlF94+jeFgutyG3iUvmxwnfw4+k+n7KmvoWL5+WTlWb1vMlwZ6RwZn5W1NYxLGGcggM9/fzT7623hYl/cwuzSUtOOqU6RvX+w7R09XF1uQ1HnY7Kkjyq953eOphwsW+8IA0M+fjkH/y9Le69dYn1tjBxLcWVRNmM3FNa8b2mvpkUl3DZgoIwRhb/qko9HDwyMOl1MOFkCSNIdz+xkTf2dHDPByoot1aTJgFUFLvZ2BjcVhWqyur6Fi6cM43cdNtD7XSMLOBbH4V1DEsYQXjg9b384bV9/NPFZ3BdpfW2MImh3OvhyMAwuw/0nPTcjU1dNBzqtdlRIXDW9BzSU5Kiso5hCeMk3tjdwV2PbvT3trjSeluYxDHS4zuYKZ6r65txJQnLbB+105bsSqKiODo78FnCOIHGQG+LkimZ/PAW621hEsuZ+dlkpLhOmjBUldV1Lbz7jKnkZaVGKLr4VlXqYVNTF/1Dw06HchxLGBPoHRjmn36/joEhH/d9eAnuDBuXNYnFlSQsKs49aY/vba097DpwxPaOCqHKEg8Dwz42N0fXzrWWMMahqvzbQ4HeFjdXMqcg2+mQjHFEebGHjU2dDA37JjxndX0zIrB8oQ1Hhcqxlq1RVvi2hDGOX7y4i8c2+HtbXL7A/hGYxFXhddM36GNH+8SF7zX1Lbxr5hQKctIjGFl8m+5OZ3puetQVvi1hjPH8lja+vWYL11RYbwtjyk9S+N7V3sOWlm4bjgqDqtLoK3xbwhhlZ3sPn/lTNQum53LPB6y3hTGzp2aRnZY84Yrv1fUtAJYwwqCyxMO+jqMc7Ol3OpRjHEsYIrJHROpEpEZE1o3zuIjID0Vkh4jUisjZ4YznuN4WH7beFsYAJAUK3xOt+F5T30JliYcZnowIRxb/RuoY0TQs5fQVxqWqWqmqS8Z57CpgbuDnduBn4QpipLfFvkBvC2+e9bYwZkSF18Pm5i4Gho4vfO/vOEpdYydX2dVFWJQXu3EliSWMIF0H/E79XgM8IhKWZaTW28KYiZUXuxkY8rGt9fgpnk9t9A9H2eru8MhIdXHW9JyoqmM4mTAUWCsib4nI7eM8XgzsH3W/IXDsOCJyu4isE5F17e3tpxzE4aMD/M+6/dbbwpgJjKz4HrseY1VdMwtn5FI61a7Iw6WyxMOG/YdPuZFVuDiZMC5Q1bPxDz3dISIXjXl8vIrzOz41Vb1XVZeo6pL8/FNv2uLJTOXxT19ovS2MmUDplExy05OPmynV0tnH+n2HbTgqzKpK8+juH2LnCaY1R5JjCUNVmwK/24CVwNIxpzQAJaPue4GmcMRS5M6w3hbGTEBEqPB6qGt8e2hkZDhqhQ1HhdXIzrXVUVLHcORbUkSyRCRn5DawHKgfc9pjwIcDs6XOAzpVtTnCoRpj8K/H2NrSTd+gf2+j1fXNzC3Itl0QwuyMaVnkpidHTR3DqbmjhcDKwBBQMvBHVV0jIh8HUNWfA6uAq4EdwFHgNodiNSbhVRS7GRxWtrZ0U5yXwRu7O/jUpXOcDivuJSUJi0s8UTNTypGEoaq7gMXjHP/5qNsK3BHJuIwx4zu24ruxk41NXfgUrrJWrBFRVeLhx8/v4Ej/kOO90m3g3hhzUsWeDKZkpVLXcJjV9c3MmprJWdNznA4rIVSV5uHTd85Sc4IlDGPMSYkI5cVuXtl5kFd3HmTFoiKbVRghi0cK31FQx7CEYYwJSoXXTcOhXoZ8atNpI2hKViqzpmZSs9/5rc4tYRhjglJe7K9jFHsyji3mM5FRWeLfudZf2nWOJQxjTFAqvP6hkSsXTrfhqAirKs2jrbuf5s4+R+OwLVmNMUGZ7k7nlx9ewpJZeU6HknBGFvDV7D/s6M7AdoVhjAnaFWWFeDJTnQ4j4SwoyiU1Ocnxlq2WMIwxJsqlJiexaEau4wv4LGEYY0wMqCrNo7ahk8Fh38lPDhNLGMYYEwMqSzz0D/nY2tJ98pPDxBKGMcbEgKrSkQV8ztUxLGEYY0wMKPZkMC07zdGtzi1hGGNMDBARqko91Di4RYglDGOMiRGVJR52HTjC4aMDjry/JQxjjIkRI3UMp6bXWsIwxpgYUeH1IGIJwxhjzElkpyUzvzDHsa3OI54wRKRERJ4Xkc0islFEPjvOOZeISKeI1AR+vhbpOI0xJhpVBlq2OrFzrRNXGEPAF1R1AXAecIeIlI1z3kuqWhn4uTuyIRpjTHSqKvXQ2TvI7gNHIv7eEU8YqtqsqusDt7uBzUBxpOMwxphYVFni3y3YiTqGozUMEZkFVAGvj/Pwu0Vkg4isFpGFJ3iN20VknYisa29vD1OkxhgTHeYUZJOV6nKkjuFYwhCRbOAh4HOq2jXm4fXATFVdDPwIeGSi11HVe1V1iaouyc/PD1/AxhgTBVxJwuJAHSPSHEkYIpKCP1k8oKoPj31cVbtUtSdwexWQIiLTIhymMcZEpcoSD5ubu+gbHI7o+zoxS0qAXwGbVfW/JzhneuA8RGQp/jgPRi5KY4yJXlWleQz5lPrGzoi+rxMtWi8AbgXqRKQmcOzLQCmAqv4c+ADwCREZAnqBm9Xp7ufGGBMlRlq2Vu87zJJZUyL2vhFPGKr6MnDCDvKq+mPgx5GJyBhjYkt+ThrevIyI1zFspbcxxsSgSgcK35YwjDEmBlWV5tF4uJe2rr6IvaclDGOMiUHH6hgRvMqwhGGMMTFo4YxcUlwS0QV8ljCMMSYGpae4KCvKpWZ/5Hp8W8IwxpgYVVWaR21DJ8O+yKw6sIRhjDExqrLEw9GBYba1dkfk/SxhGGNMjBpp2RqpOoYlDGOMiVGlUzKZkpUasTqGJQxjjIlRIkJliSdiVxhO7CVljDEmRJaXFbKh4TA+n5KUdMJdl06bJQxjjIlhNy8t5ealpRF5LxuSMsYYExRLGMYYY4JiCcMYY0xQLGEYY4wJiiUMY4wxQbGEYYwxJiiWMIwxxgTFEoYxxpigiGpktsWNBBFpB/ZO8unTgAMhDCeW2WdxPPs8jmefx9vi4bOYqar5wZwYVwnjdIjIOlVd4nQc0cA+i+PZ53E8+zzelmifhQ1JGWOMCYolDGOMMUGxhPG2e50OIIrYZ3E8+zyOZ5/H2xLqs7AahjHGmKDYFYYxxpigJHzCEJEVIrJVRHaIyJecjsdJIlIiIs+LyGYR2Sgin3U6JqeJiEtEqkXkCadjcZqIeETkLyKyJfDfyLudjslJIvL5wL+TehF5UETSnY4p3BI6YYiIC/gJcBVQBtwiImXORuWoIeALqroAOA+4I8E/D4DPApudDiJK/ABYo6pnAYtJ4M9FRIqBzwBLVHUR4AJudjaq8EvohAEsBXao6i5VHQD+BFzncEyOUdVmVV0fuN2N/wuh2NmonCMiXuC9wC+djsVpIpILXAT8CkBVB1Q1Mo2ko1cykCEiyUAm0ORwPGGX6AmjGNg/6n4DCfwFOZqIzAKqgNedjcRR3we+CPicDiQKnAG0A78ODNH9UkSynA7KKaraCHwH2Ac0A52qutbZqMIv0RPGeB3TE37amIhkAw8Bn1PVLqfjcYKIXAO0qepbTscSJZKBs4GfqWoVcARI2JqfiOThH42YDcwAskTk752NKvwSPWE0ACWj7ntJgMvKExGRFPzJ4gFVfdjpeBx0AXCtiOzBP1R5mYj8wdmQHNUANKjqyBXnX/AnkER1BbBbVdtVdRB4GDjf4ZjCLtETxpvAXBGZLSKp+ItWjzkck2NERPCPUW9W1f92Oh4nqeqdqupV1Vn4/7t4TlXj/i/IiahqC7BfROYHDl0ObHIwJKftA84TkczAv5vLSYBJAMlOB+AkVR0SkU8BT+Gf5XC/qm50OCwnXQDcCtSJSE3g2JdVdZWDMZno8WnggcAfV7uA2xyOxzGq+rqI/AVYj392YTUJsOrbVnobY4wJSqIPSRljjAmSJQxjjDFBsYRhjDEmKJYwjDHGBMUShjHGmKBYwjAGEJFhEakZ9XPCVcwi8nER+XAI3nePiEybxPOuFJGvi0ieiNi0ZxMRCb0Ow5hRelW1MtiTVfXn4QwmCO8Bnse/IeDfHI7FJAhLGMacQGBrkD8DlwYOfVBVd4jI14EeVf2OiHwG+Dj+BVybVPVmEZkC3I9/076jwO2qWisiU4EHgXzgDUbtZxbYi+gzQCr+TR8/qarDY+K5Cbgz8LrXAYVAl4icq6rXhuMzMGaEDUkZ45cxZkjqplGPdanqUuDH+HewHetLQJWqVuBPHAD/AVQHjn0Z+F3g+F3Ay4EN/B4DSgFEZAFwE3BB4EpnGPjQ2DdS1T/j38OpXlXLgfrAe1uyMGFnVxjG+J1oSOrBUb+/N87jtfi3zHgEeCRw7ELg/QCq+pyITBURN/4hpBsDx58UkUOB8y8HzgHe9G9NRAbQNkE8c4GdgduZgd4lxoSdJQxjTk4nuD3ivfgTwbXAV0VkISfeOn+81xDgt6p654kCEZF1wDQgWUQ2AUWBfb8+raovnfh/hjGnx4akjDm5m0b9fnX0AyKSBJSo6vP4my15gGzgRQJDSiJyCXAg0Ftk9PGrgLzASz0LfEBECgKPTRGRmWMDUdUlwJP46xf3AF9R1UpLFiYS7ArDGL+MUTv0gr939cjU2jQReR3/H1i3jHmeC/hDYLhJgO+p6uFAUfzXIlKLv+j9kcD5/wE8KCLrgRfwb5ONqm4SkX8H1gaS0CBwB7B3nFjPxl8c/ySQ0NvQm8iy3WqNOYHALKklqnrA6ViMcZoNSRljjAmKXWEYY4wJil1hGGOMCYolDGOMMUGxhGGMMSYoljCMMcYExRKGMcaYoFjCMMYYE5T/D0ySPQpKe+/fAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1e9ffeeb00>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# init agent\n",
"agent = Agent(state_size=state_size, action_size=action_size, seed=0)\n",
"\n",
"# load the weights from file\n",
"agent.qnetwork_local.load_state_dict(torch.load('checkpoint.pth'))\n",
"\n",
"n_episodes=10\n",
"scores=[]\n",
"for i_eps in range(1,n_episodes+1):\n",
" env_info = env.reset(train_mode=False)[brain_name] # reset env\n",
" state = env_info.vector_observations[0] # get current state\n",
" score = 0 # init score\n",
" \n",
" while True:\n",
" action = agent.act(state, eps=0) # choose action\n",
" env_info = env.step(action)[brain_name] # send action to env\n",
" next_state = env_info.vector_observations[0] # get next state of env\n",
" reward = env_info.rewards[0] # get reward from env\n",
" done = env_info.local_done[0] # check if episode is done\n",
" \n",
" score += reward\n",
" state = next_state\n",
" if done:\n",
" scores.append(score) # save most recent score\n",
" print('\\rEpisode {}\\tAverage Score: {:.2f}'.format(i_eps, np.mean(scores)), end=\"\")\n",
" break \n",
" \n",
"#env.close()\n",
"\n",
"# plot the scores\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111)\n",
"plt.plot(np.arange(len(scores)), scores)\n",
"plt.ylabel('Score')\n",
"plt.xlabel('Episode #')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}