88 lines
3.4 KiB
Python
88 lines
3.4 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: 2020-06-14 11:43:17
|
|
@Discription:
|
|
@Environment: python 3.7.7
|
|
'''
|
|
import numpy as np
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.optim as optim
|
|
|
|
from model import Actor, Critic
|
|
from memory import ReplayBuffer
|
|
|
|
|
|
class DDPG:
|
|
def __init__(self, n_states, n_actions, hidden_dim=30, device="cpu", critic_lr=1e-3,
|
|
actor_lr=1e-4, gamma=0.99, soft_tau=1e-2, memory_capacity=100000, batch_size=128):
|
|
self.device = device
|
|
self.critic = Critic(n_states, n_actions, hidden_dim).to(device)
|
|
self.actor = Actor(n_states, n_actions, hidden_dim).to(device)
|
|
self.target_critic = Critic(n_states, n_actions, hidden_dim).to(device)
|
|
self.target_actor = Actor(n_states, n_actions, hidden_dim).to(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=critic_lr)
|
|
self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=actor_lr)
|
|
self.critic_criterion = nn.MSELoss()
|
|
self.memory = ReplayBuffer(memory_capacity)
|
|
self.batch_size = batch_size
|
|
self.soft_tau = soft_tau
|
|
self.gamma = gamma
|
|
|
|
def select_action(self, state):
|
|
return self.actor.select_action(state)
|
|
|
|
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)
|
|
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 = self.critic_criterion(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
|
|
)
|