144 lines
5.9 KiB
Python
144 lines
5.9 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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'''
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@Author: John
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@Email: johnjim0816@gmail.com
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@Date: 2020-06-09 20:25:52
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@LastEditor: John
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LastEditTime: 2021-09-16 00:55:30
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@Discription:
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@Environment: python 3.7.7
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'''
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import random
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.optim as optim
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import torch.nn.functional as F
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class ReplayBuffer:
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def __init__(self, capacity):
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self.capacity = capacity # 经验回放的容量
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self.buffer = [] # 缓冲区
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self.position = 0
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def push(self, state, action, reward, next_state, done):
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''' 缓冲区是一个队列，容量超出时去掉开始存入的转移(transition)
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'''
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if len(self.buffer) < self.capacity:
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self.buffer.append(None)
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self.buffer[self.position] = (state, action, reward, next_state, done)
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self.position = (self.position + 1) % self.capacity
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def sample(self, batch_size):
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batch = random.sample(self.buffer, batch_size) # 随机采出小批量转移
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state, action, reward, next_state, done = zip(*batch) # 解压成状态，动作等
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return state, action, reward, next_state, done
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def __len__(self):
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''' 返回当前存储的量
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'''
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return len(self.buffer)
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class Actor(nn.Module):
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def __init__(self, n_states, n_actions, hidden_dim, init_w=3e-3):
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super(Actor, self).__init__()
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self.linear1 = nn.Linear(n_states, hidden_dim)
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self.linear2 = nn.Linear(hidden_dim, hidden_dim)
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self.linear3 = nn.Linear(hidden_dim, n_actions)
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self.linear3.weight.data.uniform_(-init_w, init_w)
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self.linear3.bias.data.uniform_(-init_w, init_w)
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def forward(self, x):
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x = F.relu(self.linear1(x))
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x = F.relu(self.linear2(x))
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x = torch.tanh(self.linear3(x))
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return x
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class Critic(nn.Module):
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def __init__(self, n_states, n_actions, hidden_dim, init_w=3e-3):
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super(Critic, self).__init__()
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self.linear1 = nn.Linear(n_states + n_actions, hidden_dim)
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self.linear2 = nn.Linear(hidden_dim, hidden_dim)
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self.linear3 = nn.Linear(hidden_dim, 1)
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# 随机初始化为较小的值
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self.linear3.weight.data.uniform_(-init_w, init_w)
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self.linear3.bias.data.uniform_(-init_w, init_w)
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def forward(self, state, action):
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# 按维数1拼接
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x = torch.cat([state, action], 1)
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x = F.relu(self.linear1(x))
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x = F.relu(self.linear2(x))
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x = self.linear3(x)
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return x
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class DDPG:
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def __init__(self, n_states, n_actions, cfg):
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self.device = cfg.device
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self.critic = Critic(n_states, n_actions, cfg.hidden_dim).to(cfg.device)
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self.actor = Actor(n_states, n_actions, cfg.hidden_dim).to(cfg.device)
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self.target_critic = Critic(n_states, n_actions, cfg.hidden_dim).to(cfg.device)
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self.target_actor = Actor(n_states, n_actions, cfg.hidden_dim).to(cfg.device)
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# 复制参数到目标网络
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for target_param, param in zip(self.target_critic.parameters(), self.critic.parameters()):
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target_param.data.copy_(param.data)
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for target_param, param in zip(self.target_actor.parameters(), self.actor.parameters()):
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target_param.data.copy_(param.data)
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self.critic_optimizer = optim.Adam(
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self.critic.parameters(), lr=cfg.critic_lr)
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self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=cfg.actor_lr)
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self.memory = ReplayBuffer(cfg.memory_capacity)
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self.batch_size = cfg.batch_size
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self.soft_tau = cfg.soft_tau # 软更新参数
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self.gamma = cfg.gamma
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def choose_action(self, state):
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state = torch.FloatTensor(state).unsqueeze(0).to(self.device)
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action = self.actor(state)
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return action.detach().cpu().numpy()[0, 0]
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def update(self):
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if len(self.memory) < self.batch_size: # 当 memory 中不满足一个批量时，不更新策略
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return
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# 从经验回放中(replay memory)中随机采样一个批量的转移(transition)
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state, action, reward, next_state, done = self.memory.sample(self.batch_size)
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# 转变为张量
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state = torch.FloatTensor(state).to(self.device)
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next_state = torch.FloatTensor(next_state).to(self.device)
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action = torch.FloatTensor(action).to(self.device)
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reward = torch.FloatTensor(reward).unsqueeze(1).to(self.device)
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done = torch.FloatTensor(np.float32(done)).unsqueeze(1).to(self.device)
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policy_loss = self.critic(state, self.actor(state))
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policy_loss = -policy_loss.mean()
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next_action = self.target_actor(next_state)
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target_value = self.target_critic(next_state, next_action.detach())
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expected_value = reward + (1.0 - done) * self.gamma * target_value
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expected_value = torch.clamp(expected_value, -np.inf, np.inf)
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value = self.critic(state, action)
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value_loss = nn.MSELoss()(value, expected_value.detach())
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self.actor_optimizer.zero_grad()
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policy_loss.backward()
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self.actor_optimizer.step()
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self.critic_optimizer.zero_grad()
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value_loss.backward()
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self.critic_optimizer.step()
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# 软更新
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for target_param, param in zip(self.target_critic.parameters(), self.critic.parameters()):
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target_param.data.copy_(
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target_param.data * (1.0 - self.soft_tau) +
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param.data * self.soft_tau
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)
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for target_param, param in zip(self.target_actor.parameters(), self.actor.parameters()):
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target_param.data.copy_(
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target_param.data * (1.0 - self.soft_tau) +
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param.data * self.soft_tau
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)
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def save(self,path):
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torch.save(self.actor.state_dict(), path+'checkpoint.pt')
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def load(self,path):
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self.actor.load_state_dict(torch.load(path+'checkpoint.pt')) |