连续动作空间下REINFORCE算法实现咨询（humanoid-v2环境）

Absolutely! REINFORCE (and policy gradient methods broadly) work perfectly well with continuous action spaces, and you can absolutely implement REINFORCE for OpenAI Gym's humanoid-v2 environment. Let’s walk through how this works and what you need to adjust from the discrete action space version:

The core idea of REINFORCE (maximizing the expected discounted reward via policy gradients) stays the same, but we need to tweak how we represent and sample actions:

• Policy Representation: Instead of outputting a categorical probability distribution (for discrete actions), we use a continuous probability distribution (most commonly a Gaussian distribution) to model the action space. The policy network will output the mean (mu) and standard deviation (sigma) of this distribution for each action dimension.
• Action Sampling: We sample actions from the Gaussian distribution defined by mu and sigma. To make this sampling process differentiable (so we can compute gradients through it), we use the reparameterization trick: action = mu + sigma * epsilon, where epsilon is sampled from a standard normal distribution.
• Gradient Calculation: Instead of using the log-probability of a discrete action, we use the log-probability density function (log-PDF) of the continuous action under our Gaussian distribution. The policy gradient update remains log_pdf(action) * discounted_return, with a negative sign added because we minimize loss in PyTorch/TensorFlow.

Here’s a simplified PyTorch implementation tailored to humanoid-v2:

First, import dependencies:

import gym
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from torch.distributions import Normal

Define the policy network (outputs Gaussian distribution parameters):

class PolicyNetwork(nn.Module):
    def __init__(self, state_dim, action_dim):
        super().__init__()
        # Humanoid has a high-dimensional observation space, so we use two hidden layers
        self.fc1 = nn.Linear(state_dim, 128)
        self.fc2 = nn.Linear(128, 64)
        self.mu_head = nn.Linear(64, action_dim)
        self.log_sigma_head = nn.Linear(64, action_dim)
        
    def forward(self, x):
        x = torch.tanh(self.fc1(x))
        x = torch.tanh(self.fc2(x))
        mu = self.mu_head(x)
        # Output log(sigma) instead of sigma directly to ensure sigma stays positive
        log_sigma = self.log_sigma_head(x)
        sigma = torch.exp(log_sigma)
        return mu, sigma

Implement the REINFORCE training loop:

def train_reinforce(env, policy_net, optimizer, num_episodes, gamma=0.99):
    for episode_idx in range(num_episodes):
        state = env.reset()
        episode_log_probs = []
        episode_rewards = []
        
        # Run one episode
        while True:
            # Convert state to tensor
            state_tensor = torch.tensor(state, dtype=torch.float32).unsqueeze(0)
            # Get distribution parameters from policy
            mu, sigma = policy_net(state_tensor)
            dist = Normal(mu, sigma)
            # Sample action and compute its log PDF
            action = dist.sample()
            log_prob = dist.log_prob(action).sum()  # Sum over action dimensions
            
            # Step environment
            next_state, reward, done, _ = env.step(action.detach().numpy()[0])
            
            # Store data for update
            episode_log_probs.append(log_prob)
            episode_rewards.append(reward)
            state = next_state
            
            if done:
                break
        
        # Compute discounted rewards (REINFORCE core)
        discounted_rewards = []
        running_reward = 0
        # Iterate rewards in reverse to compute cumulative discounted return
        for r in reversed(episode_rewards):
            running_reward = r + gamma * running_reward
            discounted_rewards.insert(0, running_reward)
        
        # Normalize rewards to stabilize training (critical for complex envs like Humanoid)
        discounted_rewards = torch.tensor(discounted_rewards, dtype=torch.float32)
        discounted_rewards = (discounted_rewards - discounted_rewards.mean()) / (discounted_rewards.std() + 1e-9)
        
        # Calculate policy loss
        loss = 0
        for log_prob, disc_reward in zip(episode_log_probs, discounted_rewards):
            # Negative sign because we want to maximize the objective (PyTorch minimizes loss)
            loss += -log_prob * disc_reward
        
        # Update policy network
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
        # Log progress every 10 episodes
        if episode_idx % 10 == 0:
            print(f"Episode {episode_idx:4d} | Total Reward: {sum(episode_rewards):.2f}")

# Initialize environment and components
env = gym.make('Humanoid-v2')
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.shape[0]

policy_net = PolicyNetwork(state_dim, action_dim)
optimizer = optim.Adam(policy_net.parameters(), lr=3e-4)

# Start training (expect 1000+ episodes to see meaningful progress)
train_reinforce(env, policy_net, optimizer, num_episodes=2000)

• Training Stability: REINFORCE has high variance, so reward normalization is almost mandatory here. You might also want to try adding a baseline (like a value network) to reduce variance—this evolves into the Actor-Critic algorithm, which converges faster than vanilla REINFORCE.
• Network Capacity: Humanoid has a 376-dimensional observation space and 17-dimensional action space, so the network needs enough capacity to model the complex policy. Feel free to increase hidden layer sizes or add more layers if training is slow.
• Exploration: The learned sigma controls exploration. If the agent isn’t exploring enough, you can initialize log_sigma_head to output higher values (e.g., nn.init.constant_(self.log_sigma_head.weight, 0.0) gives initial sigma=1).
• Training Time: Expect to run thousands of episodes before the agent starts walking coherently—continuous control tasks are computationally intensive!

内容的提问来源于stack exchange，提问作者Pythoncoder