policy gradient实现mountain_car的代码

下面是使用Policy Gradient算法实现Mountain Car问题的代码，使用的是PyTorch框架：

import gym
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F


class Policy(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(Policy, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = F.softmax(self.fc2(x), dim=1)
        return x


def select_action(state):
    state = torch.from_numpy(state).float().unsqueeze(0)
    probs = policy(state)
    m = torch.distributions.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 + 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[:]


env = gym.make('MountainCar-v0')
gamma = 0.99
eps = np.finfo(np.float32).eps.item()
policy = Policy(env.observation_space.shape[0], 128, env.action_space.n)
optimizer = optim.Adam(policy.parameters(), lr=1e-2)

running_reward = 10
for i_episode in range(1000):
    state, ep_reward = env.reset(), 0
    for t in range(10000):
        action = select_action(state)
        state, reward, done, _ = env.step(action)
        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 % 50 == 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

这段代码使用了PyTorch的神经网络模块来构建Policy Gradient算法的策略网络，并在Mountain Car环境中进行训练。具体实现过程可以参考注释。