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OpenAI Gym CartPole-v1 with Pytorch 1.0
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Policy Gradients on CartPole with PyTorch 1.0"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import gym\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.optim as optim\n",
"import torch.nn.functional as F\n",
"from torch.distributions import Categorical"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"env = gym.make('CartPole-v1')\n",
"env.seed(1)\n",
"torch.manual_seed(1)\n",
"\n",
"# Hyperparameters\n",
"learning_rate = 0.01\n",
"gamma = 0.99"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Define model and training loop"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"class Policy(nn.Module):\n",
" def __init__(self):\n",
" super(Policy, self).__init__()\n",
" state_space = env.observation_space.shape[0]\n",
" action_space = env.action_space.n\n",
" num_hidden = 128\n",
"\n",
" self.l1 = nn.Linear(state_space, num_hidden, bias=False)\n",
" self.l2 = nn.Linear(num_hidden, action_space, bias=False)\n",
"\n",
" # Overall reward and loss history\n",
" self.reward_history = []\n",
" self.loss_history = []\n",
" self.reset()\n",
"\n",
" def reset(self):\n",
" # Episode policy and reward history\n",
" self.episode_actions = torch.Tensor([])\n",
" self.episode_rewards = []\n",
"\n",
" def forward(self, x):\n",
" model = torch.nn.Sequential(\n",
" self.l1,\n",
" nn.Dropout(p=0.5),\n",
" nn.ReLU(),\n",
" self.l2,\n",
" nn.Softmax(dim=-1)\n",
" )\n",
" return model(x)\n",
"\n",
"\n",
"def predict(state):\n",
" # Select an action (0 or 1) by running policy model\n",
" # and choosing based on the probabilities in state\n",
" state = torch.from_numpy(state).type(torch.FloatTensor)\n",
" action_probs = policy(state)\n",
" distribution = Categorical(action_probs)\n",
" action = distribution.sample()\n",
"\n",
" # Add log probability of our chosen action to our history\n",
" policy.episode_actions = torch.cat([\n",
" policy.episode_actions,\n",
" distribution.log_prob(action).reshape(1)\n",
" ])\n",
"\n",
" return action\n",
"\n",
"\n",
"def update_policy():\n",
" R = 0\n",
" rewards = []\n",
"\n",
" # Discount future rewards back to the present using gamma\n",
" for r in policy.episode_rewards[::-1]:\n",
" R = r + gamma * R\n",
" rewards.insert(0, R)\n",
"\n",
" # Scale rewards\n",
" rewards = torch.FloatTensor(rewards)\n",
" rewards = (rewards - rewards.mean()) / \\\n",
" (rewards.std() + np.finfo(np.float32).eps)\n",
"\n",
" # Calculate loss\n",
" loss = (torch.sum(torch.mul(policy.episode_actions, rewards).mul(-1), -1))\n",
"\n",
" # Update network weights\n",
" optimizer.zero_grad()\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" # Save and intialize episode history counters\n",
" policy.loss_history.append(loss.item())\n",
" policy.reward_history.append(np.sum(policy.episode_rewards))\n",
" policy.reset()\n",
"\n",
"\n",
"def train(episodes):\n",
" scores = []\n",
" for episode in range(episodes):\n",
" # Reset environment and record the starting state\n",
" state = env.reset()\n",
"\n",
" for time in range(1000):\n",
" action = predict(state)\n",
"\n",
" # Uncomment to render the visual state in a window\n",
" # env.render()\n",
"\n",
" # Step through environment using chosen action\n",
" state, reward, done, _ = env.step(action.item())\n",
"\n",
" # Save reward\n",
" policy.episode_rewards.append(reward)\n",
" if done:\n",
" break\n",
"\n",
" update_policy()\n",
"\n",
" # Calculate score to determine when the environment has been solved\n",
" scores.append(time)\n",
" mean_score = np.mean(scores[-100:])\n",
"\n",
" if episode % 50 == 0:\n",
" print('Episode {}\\tAverage length (last 100 episodes): {:.2f}'.format(\n",
" episode, mean_score))\n",
"\n",
" if mean_score > env.spec.reward_threshold:\n",
" print(\"Solved after {} episodes! Running average is now {}. Last episode ran to {} time steps.\"\n",
" .format(episode, mean_score, time))\n",
" break"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Start training"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Episode 0\tAverage length (last 100 episodes): 8.00\n",
"Episode 50\tAverage length (last 100 episodes): 24.22\n",
"Episode 100\tAverage length (last 100 episodes): 63.07\n",
"Episode 150\tAverage length (last 100 episodes): 120.44\n",
"Episode 200\tAverage length (last 100 episodes): 275.69\n",
"Episode 250\tAverage length (last 100 episodes): 447.72\n",
"Episode 300\tAverage length (last 100 episodes): 440.06\n",
"Episode 350\tAverage length (last 100 episodes): 447.89\n",
"Solved after 379 episodes! Running average is now 475.86. Last episode ran to 499 time steps.\n"
]
}
],
"source": [
"policy = Policy()\n",
"optimizer = optim.Adam(policy.parameters(), lr=learning_rate)\n",
"train(episodes=1000)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plot training performance"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 648x648 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# number of episodes for rolling average\n",
"window = 50\n",
"\n",
"fig, ((ax1), (ax2)) = plt.subplots(2, 1, sharey=True, figsize=[9, 9])\n",
"rolling_mean = pd.Series(policy.reward_history).rolling(window).mean()\n",
"std = pd.Series(policy.reward_history).rolling(window).std()\n",
"ax1.plot(rolling_mean)\n",
"ax1.fill_between(range(len(policy.reward_history)), rolling_mean -\n",
" std, rolling_mean+std, color='orange', alpha=0.2)\n",
"ax1.set_title(\n",
" 'Episode Length Moving Average ({}-episode window)'.format(window))\n",
"ax1.set_xlabel('Episode')\n",
"ax1.set_ylabel('Episode Length')\n",
"\n",
"ax2.plot(policy.reward_history)\n",
"ax2.set_title('Episode Length')\n",
"ax2.set_xlabel('Episode')\n",
"ax2.set_ylabel('Episode Length')\n",
"\n",
"fig.tight_layout(pad=2)\n",
"plt.show()"
]
}
],
"metadata": {
"file_extension": ".py",
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
},
"mimetype": "text/x-python",
"name": "python",
"npconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": 3
},
"nbformat": 4,
"nbformat_minor": 2
}
appnope==0.1.0
astroid==2.1.0
autopep8==1.4.3
backcall==0.1.0
bleach==3.0.2
certifi==2018.11.29
chardet==3.0.4
cycler==0.10.0
decorator==4.3.0
defusedxml==0.5.0
entrypoints==0.2.3
future==0.17.1
gym==0.10.9
idna==2.7
ipykernel==5.1.0
ipython==7.2.0
ipython-genutils==0.2.0
ipywidgets==7.4.2
isort==4.3.4
jedi==0.13.1
Jinja2==2.10
jsonschema==2.6.0
jupyter==1.0.0
jupyter-client==5.2.4
jupyter-console==6.0.0
jupyter-core==4.4.0
kiwisolver==1.0.1
lazy-object-proxy==1.3.1
MarkupSafe==1.1.0
matplotlib==3.0.2
mccabe==0.6.1
mistune==0.8.4
nbconvert==5.4.0
nbformat==4.4.0
notebook==5.7.2
numpy==1.15.4
pandas==0.23.4
pandocfilters==1.4.2
parso==0.3.1
pexpect==4.6.0
pickleshare==0.7.5
Pillow==5.3.0
prometheus-client==0.5.0
prompt-toolkit==2.0.7
ptyprocess==0.6.0
pycodestyle==2.4.0
pyglet==1.3.2
Pygments==2.3.0
pylint==2.2.2
pyparsing==2.3.0
python-dateutil==2.7.5
pytz==2018.7
pyzmq==17.1.2
qtconsole==4.4.3
requests==2.20.1
rope==0.11.0
scipy==1.1.0
Send2Trash==1.5.0
six==1.11.0
terminado==0.8.1
testpath==0.4.2
torch==1.0.0
torchvision==0.2.1
tornado==5.1.1
tqdm==4.28.1
traitlets==4.3.2
urllib3==1.24.1
wcwidth==0.1.7
webencodings==0.5.1
widgetsnbextension==3.4.2
wrapt==1.10.11
@brando90

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commented Feb 15, 2019

is there a visualization of the Pole and Cart?

@brando90

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commented Feb 16, 2019

python question. How does the code know what policy is if its not passed in as a variable AND its not at the top of the file? dark magic!

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