178 lines
4.9 KiB
Python
178 lines
4.9 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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'''
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Author: JiangJi
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Email: johnjim0816@gmail.com
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Date: 2021-12-22 10:40:05
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LastEditor: JiangJi
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LastEditTime: 2021-12-22 10:43:55
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Discription:
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'''
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import copy
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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.nn.functional as F
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from TD3.memory import ReplayBuffer
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class Actor(nn.Module):
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def __init__(self, input_dim, output_dim, max_action):
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'''[summary]
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Args:
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input_dim (int): 输入维度，这里等于n_states
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output_dim (int): 输出维度，这里等于n_actions
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max_action (int): action的最大值
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'''
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super(Actor, self).__init__()
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self.l1 = nn.Linear(input_dim, 256)
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self.l2 = nn.Linear(256, 256)
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self.l3 = nn.Linear(256, output_dim)
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self.max_action = max_action
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def forward(self, state):
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a = F.relu(self.l1(state))
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a = F.relu(self.l2(a))
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return self.max_action * torch.tanh(self.l3(a))
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class Critic(nn.Module):
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def __init__(self, input_dim, output_dim):
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super(Critic, self).__init__()
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# Q1 architecture
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self.l1 = nn.Linear(input_dim + output_dim, 256)
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self.l2 = nn.Linear(256, 256)
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self.l3 = nn.Linear(256, 1)
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# Q2 architecture
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self.l4 = nn.Linear(input_dim + output_dim, 256)
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self.l5 = nn.Linear(256, 256)
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self.l6 = nn.Linear(256, 1)
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def forward(self, state, action):
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sa = torch.cat([state, action], 1)
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q1 = F.relu(self.l1(sa))
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q1 = F.relu(self.l2(q1))
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q1 = self.l3(q1)
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q2 = F.relu(self.l4(sa))
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q2 = F.relu(self.l5(q2))
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q2 = self.l6(q2)
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return q1, q2
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def Q1(self, state, action):
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sa = torch.cat([state, action], 1)
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q1 = F.relu(self.l1(sa))
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q1 = F.relu(self.l2(q1))
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q1 = self.l3(q1)
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return q1
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class TD3(object):
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def __init__(
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self,
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input_dim,
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output_dim,
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max_action,
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cfg,
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):
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self.max_action = max_action
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self.gamma = cfg.gamma
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self.lr = cfg.lr
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self.policy_noise = cfg.policy_noise
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self.noise_clip = cfg.noise_clip
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self.policy_freq = cfg.policy_freq
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self.batch_size = cfg.batch_size
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self.device = cfg.device
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self.total_it = 0
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self.actor = Actor(input_dim, output_dim, max_action).to(self.device)
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self.actor_target = copy.deepcopy(self.actor)
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self.actor_optimizer = torch.optim.Adam(self.actor.parameters(), lr=3e-4)
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self.critic = Critic(input_dim, output_dim).to(self.device)
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self.critic_target = copy.deepcopy(self.critic)
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self.critic_optimizer = torch.optim.Adam(self.critic.parameters(), lr=3e-4)
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self.memory = ReplayBuffer(input_dim, output_dim)
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def choose_action(self, state):
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state = torch.FloatTensor(state.reshape(1, -1)).to(self.device)
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return self.actor(state).cpu().data.numpy().flatten()
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def update(self):
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self.total_it += 1
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# Sample replay buffer
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state, action, next_state, reward, not_done = self.memory.sample(self.batch_size)
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with torch.no_grad():
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# Select action according to policy and add clipped noise
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noise = (
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torch.randn_like(action) * self.policy_noise
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).clamp(-self.noise_clip, self.noise_clip)
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next_action = (
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self.actor_target(next_state) + noise
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).clamp(-self.max_action, self.max_action)
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# Compute the target Q value
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target_Q1, target_Q2 = self.critic_target(next_state, next_action)
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target_Q = torch.min(target_Q1, target_Q2)
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target_Q = reward + not_done * self.gamma * target_Q
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# Get current Q estimates
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current_Q1, current_Q2 = self.critic(state, action)
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# Compute critic loss
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critic_loss = F.mse_loss(current_Q1, target_Q) + F.mse_loss(current_Q2, target_Q)
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# Optimize the critic
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self.critic_optimizer.zero_grad()
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critic_loss.backward()
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self.critic_optimizer.step()
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# Delayed policy updates
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if self.total_it % self.policy_freq == 0:
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# Compute actor losse
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actor_loss = -self.critic.Q1(state, self.actor(state)).mean()
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# Optimize the actor
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self.actor_optimizer.zero_grad()
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actor_loss.backward()
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self.actor_optimizer.step()
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# Update the frozen target models
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for param, target_param in zip(self.critic.parameters(), self.critic_target.parameters()):
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target_param.data.copy_(self.lr * param.data + (1 - self.lr) * target_param.data)
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for param, target_param in zip(self.actor.parameters(), self.actor_target.parameters()):
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target_param.data.copy_(self.lr * param.data + (1 - self.lr) * target_param.data)
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def save(self, path):
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torch.save(self.critic.state_dict(), path + "td3_critic")
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torch.save(self.critic_optimizer.state_dict(), path + "td3_critic_optimizer")
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torch.save(self.actor.state_dict(), path + "td3_actor")
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torch.save(self.actor_optimizer.state_dict(), path + "td3_actor_optimizer")
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def load(self, path):
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self.critic.load_state_dict(torch.load(path + "td3_critic"))
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self.critic_optimizer.load_state_dict(torch.load(path + "td3_critic_optimizer"))
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self.critic_target = copy.deepcopy(self.critic)
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self.actor.load_state_dict(torch.load(path + "td3_actor"))
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self.actor_optimizer.load_state_dict(torch.load(path + "td3_actor_optimizer"))
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self.actor_target = copy.deepcopy(self.actor)
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