actor critic pytorch

Actor-Critic是一种强化学习算法，它结合了策略梯度方法和值函数方法。在PyTorch中，可以使用深度神经网络来实现Actor-Critic算法。其中，Actor网络用于输出动作概率分布，Critic网络用于估计状态值函数。通过训练Actor和Critic网络，可以使得智能体在环境中学习到最优的策略。

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actor-critic pytorch

actor-critic pytorch 是基于 PyTorch 框架实现的一种增强学习算法，其中 actor 和 critic 分别代表策略网络和价值网络。该算法通过同时优化策略和价值函数，以提高智能体的决策效果和长期累积回报。这种算法在多个领域都有广泛的应用，包括游戏、金融、机器人等。

多智能体编队actor-critic pytorch代码

以下是一个简单的多智能体编队actor-critic算法的PyTorch实现代码。该代码演示了如何使用actor-critic算法来训练多个智能体以形成编队。该代码使用一个简单的环境，该环境包含一个目标点和多个智能体，智能体的任务是在不碰撞的情况下尽可能接近目标点。

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import numpy as np
import random

# 定义一个智能体类
class Agent:
    def __init__(self, state_size, action_size, agent_id, num_agents):
        self.state_size = state_size
        self.action_size = action_size
        self.agent_id = agent_id
        self.num_agents = num_agents
        
        # 定义actor网络和critic网络
        self.actor = Actor(state_size, action_size)
        self.critic = Critic(state_size)
        
        # 定义优化器
        self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=0.001)
        self.critic_optimizer = optim.Adam(self.critic.parameters(), lr=0.001)
        
        # 定义经验回放缓冲区
        self.memory = ReplayBuffer(action_size)
        
    def act(self, state):
        state = torch.from_numpy(state).float().unsqueeze(0)
        action_probs = F.softmax(self.actor(state), dim=1)
        action_probs = action_probs.detach().numpy().squeeze()
        action = np.random.choice(self.action_size, p=action_probs)
        return action
    
    def learn(self, experiences, gamma):
        states, actions, rewards, next_states, dones = experiences
        
        # 计算critic网络的损失
        Qvals = self.critic(states)
        Qvals_next = self.critic(next_states)
        Qval = Qvals.gather(1, actions)
        Qval_next = rewards + gamma * Qvals_next.max(1)[0].unsqueeze(1) * (1 - dones)
        critic_loss = F.mse_loss(Qval, Qval_next.detach())
        
        # 更新critic网络
        self.critic_optimizer.zero_grad()
        critic_loss.backward()
        self.critic_optimizer.step()
        
        # 计算actor网络的损失
        probs = F.softmax(self.actor(states), dim=1)
        log_probs = torch.log(probs.gather(1, actions))
        Qvals = self.critic(states)
        advantages = Qvals.detach() - Qvals.mean()
        actor_loss = -(log_probs * advantages).mean()
        
        # 更新actor网络
        self.actor_optimizer.zero_grad()
        actor_loss.backward()
        self.actor_optimizer.step()

# 定义一个actor网络
class Actor(nn.Module):
    def __init__(self, state_size, action_size):
        super(Actor, self).__init__()
        self.fc1 = nn.Linear(state_size, 32)
        self.fc2 = nn.Linear(32, 64)
        self.fc3 = nn.Linear(64, action_size)
        
    def forward(self, state):
        x = F.relu(self.fc1(state))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# 定义一个critic网络
class Critic(nn.Module):
    def __init__(self, state_size):
        super(Critic, self).__init__()
        self.fc1 = nn.Linear(state_size, 32)
        self.fc2 = nn.Linear(32, 64)
        self.fc3 = nn.Linear(64, 1)
        
    def forward(self, state):
        x = F.relu(self.fc1(state))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# 定义一个经验回放缓冲区
class ReplayBuffer:
    def __init__(self, action_size, buffer_size=10000, batch_size=128):
        self.action_size = action_size
        self.buffer_size = buffer_size
        self.batch_size = batch_size
        self.memory = []
        self.position = 0
        
    def add(self, state, action, reward, next_state, done):
        experience = (state, action, reward, next_state, done)
        if len(self.memory) < self.buffer_size:
            self.memory.append(None)
        self.memory[self.position] = experience
        self.position = (self.position + 1) % self.buffer_size
        
    def sample(self):
        experiences = random.sample(self.memory, k=self.batch_size)
        states = torch.from_numpy(np.vstack([e[0] for e in experiences if e is not None])).float()
        actions = torch.from_numpy(np.vstack([e[1] for e in experiences if e is not None])).long()
        rewards = torch.from_numpy(np.vstack([e[2] for e in experiences if e is not None])).float()
        next_states = torch.from_numpy(np.vstack([e[3] for e in experiences if e is not None])).float()
        dones = torch.from_numpy(np.vstack([e[4] for e in experiences if e is not None]).astype(np.uint8)).float()
        return (states, actions, rewards, next_states, dones)

# 定义一个环境类
class Env:
    def __init__(self, num_agents):
        self.num_agents = num_agents
        self.state_size = 4
        self.action_size = 2
        self.target_pos = np.array([0.0, 0.0])
        self.agent_pos = np.random.uniform(-1, 1, size=(self.num_agents, 2))
        
    def reset(self):
        self.target_pos = np.array([0.0, 0.0])
        self.agent_pos = np.random.uniform(-1, 1, size=(self.num_agents, 2))
        obs = np.hstack([self.agent_pos, self.target_pos])
        return obs
    
    def step(self, actions):
        actions = np.clip(actions, -1, 1)
        self.agent_pos += actions
        self.agent_pos = np.clip(self.agent_pos, -1, 1)
        obs = np.hstack([self.agent_pos, self.target_pos])
        rewards = np.zeros(self.num_agents)
        for i in range(self.num_agents):
            dist = np.linalg.norm(self.agent_pos[i] - self.target_pos)
            if dist < 0.1:
                rewards[i] = 1
        dones = np.zeros(self.num_agents)
        return obs, rewards, dones

# 定义一个多智能体编队类
class MultiAgentFormation:
    def __init__(self, num_agents):
        self.env = Env(num_agents)
        self.num_agents = num_agents
        self.state_size = self.env.state_size * self.num_agents
        self.action_size = self.env.action_size
        self.gamma = 0.99
        self.agents = [Agent(self.env.state_size, self.env.action_size, i, self.num_agents) for i in range(self.num_agents)]
        
    def train(self, num_episodes=1000, max_t=1000):
        for i_episode in range(num_episodes):
            obs = self.env.reset()
            for t in range(max_t):
                actions = []
                for i in range(self.num_agents):
                    action = self.agents[i].act(obs[i])
                    actions.append(action)
                next_obs, rewards, dones = self.env.step(actions)
                for i in range(self.num_agents):
                    self.agents[i].memory.add(obs[i], actions[i], rewards[i], next_obs[i], dones[i])
                obs = next_obs
                if len(self.agents[0].memory) > self.agents[0].memory.batch_size:
                    for agent in self.agents:
                        experiences = agent.memory.sample()
                        agent.learn(experiences, self.gamma)
                if np.any(dones):
                    break

# 测试
ma = MultiAgentFormation(num_agents=3)
ma.train()