91 lines
3.6 KiB
Python
91 lines
3.6 KiB
Python
#!/usr/bin/env python
|
|
# coding=utf-8
|
|
'''
|
|
@Author: John
|
|
@Email: johnjim0816@gmail.com
|
|
@Date: 2020-06-09 20:25:52
|
|
@LastEditor: John
|
|
LastEditTime: 2021-03-31 00:56:32
|
|
@Discription:
|
|
@Environment: python 3.7.7
|
|
'''
|
|
import numpy as np
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.optim as optim
|
|
|
|
from common.model import Actor, Critic
|
|
from common.memory import ReplayBuffer
|
|
|
|
|
|
class DDPG:
|
|
def __init__(self, state_dim, action_dim, cfg):
|
|
self.device = cfg.device
|
|
self.critic = Critic(state_dim, action_dim, cfg.hidden_dim).to(cfg.device)
|
|
self.actor = Actor(state_dim, action_dim, cfg.hidden_dim).to(cfg.device)
|
|
self.target_critic = Critic(state_dim, action_dim, cfg.hidden_dim).to(cfg.device)
|
|
self.target_actor = Actor(state_dim, action_dim, cfg.hidden_dim).to(cfg.device)
|
|
|
|
for target_param, param in zip(self.target_critic.parameters(), self.critic.parameters()):
|
|
target_param.data.copy_(param.data)
|
|
for target_param, param in zip(self.target_actor.parameters(), self.actor.parameters()):
|
|
target_param.data.copy_(param.data)
|
|
|
|
self.critic_optimizer = optim.Adam(
|
|
self.critic.parameters(), lr=cfg.critic_lr)
|
|
self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=cfg.actor_lr)
|
|
self.memory = ReplayBuffer(cfg.memory_capacity)
|
|
self.batch_size = cfg.batch_size
|
|
self.soft_tau = cfg.soft_tau
|
|
self.gamma = cfg.gamma
|
|
|
|
def choose_action(self, state):
|
|
state = torch.FloatTensor(state).unsqueeze(0).to(self.device)
|
|
action = self.actor(state)
|
|
# torch.detach()用于切断反向传播
|
|
return action.detach().cpu().numpy()[0, 0]
|
|
|
|
def update(self):
|
|
if len(self.memory) < self.batch_size:
|
|
return
|
|
state, action, reward, next_state, done = self.memory.sample(
|
|
self.batch_size)
|
|
# 将所有变量转为张量
|
|
state = torch.FloatTensor(state).to(self.device)
|
|
next_state = torch.FloatTensor(next_state).to(self.device)
|
|
action = torch.FloatTensor(action).to(self.device)
|
|
reward = torch.FloatTensor(reward).unsqueeze(1).to(self.device)
|
|
done = torch.FloatTensor(np.float32(done)).unsqueeze(1).to(self.device)
|
|
# 注意critic将(s_t,a)作为输入
|
|
policy_loss = self.critic(state, self.actor(state))
|
|
policy_loss = -policy_loss.mean()
|
|
next_action = self.target_actor(next_state)
|
|
target_value = self.target_critic(next_state, next_action.detach())
|
|
expected_value = reward + (1.0 - done) * self.gamma * target_value
|
|
expected_value = torch.clamp(expected_value, -np.inf, np.inf)
|
|
|
|
value = self.critic(state, action)
|
|
value_loss = nn.MSELoss()(value, expected_value.detach())
|
|
|
|
self.actor_optimizer.zero_grad()
|
|
policy_loss.backward()
|
|
self.actor_optimizer.step()
|
|
|
|
self.critic_optimizer.zero_grad()
|
|
value_loss.backward()
|
|
self.critic_optimizer.step()
|
|
for target_param, param in zip(self.target_critic.parameters(), self.critic.parameters()):
|
|
target_param.data.copy_(
|
|
target_param.data * (1.0 - self.soft_tau) +
|
|
param.data * self.soft_tau
|
|
)
|
|
for target_param, param in zip(self.target_actor.parameters(), self.actor.parameters()):
|
|
target_param.data.copy_(
|
|
target_param.data * (1.0 - self.soft_tau) +
|
|
param.data * self.soft_tau
|
|
)
|
|
def save(self,path):
|
|
torch.save(self.actor.state_dict(), path+'checkpoint.pt')
|
|
|
|
def load(self,path):
|
|
self.actor.load_state_dict(torch.load(path+'checkpoint.pt')) |