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July 23, 2021 15:45
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REINFORCE algorithm in PyTorch
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| import argparse | |
| import gym | |
| import numpy as np | |
| from itertools import count | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import torch.optim as optim | |
| from torch.distributions import Categorical | |
| parser = argparse.ArgumentParser(description='PyTorch REINFORCE example') | |
| parser.add_argument('--gamma', type=float, default=0.99, metavar='G', | |
| help='discount factor (default: 0.99)') | |
| parser.add_argument('--seed', type=int, default=543, metavar='N', | |
| help='random seed (default: 543)') | |
| parser.add_argument('--render', action='store_true', | |
| help='render the environment') | |
| parser.add_argument('--log-interval', type=int, default=10, metavar='N', | |
| help='interval between training status logs (default: 10)') | |
| args = parser.parse_args() | |
| env = gym.make('CartPole-v1') | |
| env.seed(args.seed) | |
| torch.manual_seed(args.seed) | |
| class Policy(nn.Module): | |
| def __init__(self): | |
| super(Policy, self).__init__() | |
| self.affine1 = nn.Linear(4, 128) | |
| self.dropout = nn.Dropout(p=0.6) | |
| self.affine2 = nn.Linear(128, 2) | |
| self.saved_log_probs = [] | |
| self.rewards = [] | |
| def forward(self, x): | |
| x = self.affine1(x) | |
| x = self.dropout(x) | |
| x = F.relu(x) | |
| action_scores = self.affine2(x) | |
| return F.softmax(action_scores, dim=1) | |
| policy = Policy() | |
| optimizer = optim.Adam(policy.parameters(), lr=1e-2) | |
| eps = np.finfo(np.float32).eps.item() | |
| def select_action(state): | |
| state = torch.from_numpy(state).float().unsqueeze(0) | |
| probs = policy(state) | |
| m = Categorical(probs) | |
| action = m.sample() | |
| policy.saved_log_probs.append(m.log_prob(action)) | |
| return action.item() | |
| def finish_episode(): | |
| R = 0 | |
| policy_loss = [] | |
| returns = [] | |
| for r in policy.rewards[::-1]: | |
| R = r + args.gamma * R | |
| returns.insert(0, R) | |
| returns = torch.tensor(returns) | |
| returns = (returns - returns.mean()) / (returns.std() + eps) | |
| for log_prob, R in zip(policy.saved_log_probs, returns): | |
| policy_loss.append(-log_prob * R) | |
| optimizer.zero_grad() | |
| policy_loss = torch.cat(policy_loss).sum() | |
| policy_loss.backward() | |
| optimizer.step() | |
| del policy.rewards[:] | |
| del policy.saved_log_probs[:] | |
| def main(): | |
| running_reward = 10 | |
| for i_episode in count(1): | |
| state, ep_reward = env.reset(), 0 | |
| # state: Tsr[4] | |
| for t in range(1, 10000): # Don't infinite loop while learning | |
| action = select_action(state) | |
| state, reward, done, _ = env.step(action) | |
| if args.render: | |
| env.render() | |
| policy.rewards.append(reward) | |
| ep_reward += reward | |
| if done: | |
| break | |
| running_reward = 0.05 * ep_reward + (1 - 0.05) * running_reward | |
| finish_episode() | |
| if i_episode % args.log_interval == 0: | |
| print('Episode {}\tLast reward: {:.2f}\tAverage reward: {:.2f}'.format( | |
| i_episode, ep_reward, running_reward)) | |
| if running_reward > env.spec.reward_threshold: | |
| print("Solved! Running reward is now {} and " | |
| "the last episode runs to {} time steps!".format(running_reward, t)) | |
| break | |
| if __name__ == '__main__': | |
| main() |
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