强化学习之TD3（pytorch实现）

参考视频：https://www.bilibili.com/video/BV1EK41157fD/?spm_id_from=333.788.recommend_more_video.-1

原论文：https://arxiv.org/abs/1802.09477

价值函数优化学习主线：Q-learning→DQN→DDPG→TD3→SAC

其中SAC和TD3算是目前很好的两个强化学习算法了，有必要掌握。
Q-Learning，DQN和DDPG请可以参考我之前的文章：强化学习实践教学

首先DDPG是对DQN的扩展，使得只能用于离散动作空间的DQN扩展到连续动作空间，在方法上同样运用了经验回放和Target网络，同时是一个Actor-Critic方法，因此存在四个网络。

TD3也叫做Twin Delayed DDPG，全称Twin Delayed Deep Deterministic Policy Gradient。是基于DDPG的改进。同样DDPG也存在着跟DQN相同的缺陷，就是由于采用的是max最大动作价值的方式进行更新，使得动作价值远比实际要高（overestinmate），使得训练不稳定。因此TD3提出了三种改进。

Clipped Double_Q Learning

TD3使用了两个Q网络进行学习，因此是“twin”。而且采用两个网络中动作价值Q较小的值进行更新。两个网络同时使用一个target网络，因此它们同时都会产生一个预测值，我们只用预测值中较小的来对网络进行更新。

“Delayed” Policy Updates

TD3更新策略网络（及其Target网络）的频率要比价值网络慢，论文中推荐更新两次价值网络才更新一次策略网络。这样可以让两者解耦，关联度降低，从而可以克服overestinmation。

Target Policy Smoothing

TD3在策略网络的target网络中引入了噪声，让其更加积极去探索Q价值网络的错误。
$$a_{TD3}(s^{\prime })=clip({\mu }_{\theta ,targ}(s^{\prime })+clip(ϵ,-c,c),a_{low},a_{high}),ϵ～N(0,\sigma )$$

Pytorch实现

import copy
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F


device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Implementation of Twin Delayed Deep Deterministic Policy Gradients (TD3)
# Paper: https://arxiv.org/abs/1802.09477


class Actor(nn.Module):
	def __init__(self, state_dim, action_dim, max_action):
		super(Actor, self).__init__()

		self.l1 = nn.Linear(state_dim, 256)
		self.l2 = nn.Linear(256, 256)
		self.l3 = nn.Linear(256, action_dim)
		
		self.max_action = max_action
		

	def forward(self, state):
		a = F.relu(self.l1(state))
		a = F.relu(self.l2(a))
		return self.max_action * torch.tanh(self.l3(a))


class Critic(nn.Module):
	def __init__(self, state_dim, action_dim):
		super(Critic, self).__init__()

		# Q1 architecture
		self.l1 = nn.Linear(state_dim + action_dim, 256)
		self.l2 = nn.Linear(256, 256)
		self.l3 = nn.Linear(256, 1)

		# Q2 architecture
		self.l4 = nn.Linear(state_dim + action_dim, 256)
		self.l5 = nn.Linear(256, 256)
		self.l6 = nn.Linear(256, 1)


	def forward(self, state, action):
		sa = torch.cat([state, action], 1)

		q1 = F.relu(self.l1(sa))
		q1 = F.relu(self.l2(q1))
		q1 = self.l3(q1)

		q2 = F.relu(self.l4(sa))
		q2 = F.relu(self.l5(q2))
		q2 = self.l6(q2)
		return q1, q2


	def Q1(self, state, action):
		sa = torch.cat([state, action], 1)

		q1 = F.relu(self.l1(sa))
		q1 = F.relu(self.l2(q1))
		q1 = self.l3(q1)
		return q1


class TD3(object):
	def __init__(
		self,
		state_dim,
		action_dim,
		max_action,
		discount=0.99,
		tau=0.005,
		policy_noise=0.2,
		noise_clip=0.5,
		policy_freq=2
	):

		self.actor = Actor(state_dim, action_dim, max_action).to(device)
		self.actor_target = copy.deepcopy(self.actor)
		self.actor_optimizer = torch.optim.Adam(self.actor.parameters(), lr=3e-4)

		self.critic = Critic(state_dim, action_dim).to(device)
		self.critic_target = copy.deepcopy(self.critic)
		self.critic_optimizer = torch.optim.Adam(self.critic.parameters(), lr=3e-4)

		self.max_action = max_action
		self.discount = discount
		self.tau = tau
		self.policy_noise = policy_noise
		self.noise_clip = noise_clip
		self.policy_freq = policy_freq

		self.total_it = 0


	def select_action(self, state):
		state = torch.FloatTensor(state.reshape(1, -1)).to(device)
		return self.actor(state).cpu().data.numpy().flatten()


	def train(self, replay_buffer, batch_size=256):
		self.total_it += 1

		# Sample replay buffer 
		state, action, next_state, reward, not_done = replay_buffer.sample(batch_size)

		with torch.no_grad():
			# Select action according to policy and add clipped noise
			noise = (
				torch.randn_like(action) * self.policy_noise
			).clamp(-self.noise_clip, self.noise_clip)
			
			next_action = (
				self.actor_target(next_state) + noise
			).clamp(-self.max_action, self.max_action)

			# Compute the target Q value
			target_Q1, target_Q2 = self.critic_target(next_state, next_action)
			target_Q = torch.min(target_Q1, target_Q2)
			target_Q = reward + not_done * self.discount * target_Q

		# Get current Q estimates
		current_Q1, current_Q2 = self.critic(state, action)

		# Compute critic loss
		critic_loss = F.mse_loss(current_Q1, target_Q) + F.mse_loss(current_Q2, target_Q)

		# Optimize the critic
		self.critic_optimizer.zero_grad()
		critic_loss.backward()
		self.critic_optimizer.step()

		# Delayed policy updates
		if self.total_it % self.policy_freq == 0:

			# Compute actor losse
			actor_loss = -self.critic.Q1(state, self.actor(state)).mean()
			
			# Optimize the actor 
			self.actor_optimizer.zero_grad()
			actor_loss.backward()
			self.actor_optimizer.step()

			# Update the frozen target models
			for param, target_param in zip(self.critic.parameters(), self.critic_target.parameters()):
				target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)

			for param, target_param in zip(self.actor.parameters(), self.actor_target.parameters()):
				target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)


	def save(self, filename):
		torch.save(self.critic.state_dict(), filename + "_critic")
		torch.save(self.critic_optimizer.state_dict(), filename + "_critic_optimizer")
		
		torch.save(self.actor.state_dict(), filename + "_actor")
		torch.save(self.actor_optimizer.state_dict(), filename + "_actor_optimizer")


	def load(self, filename):
		self.critic.load_state_dict(torch.load(filename + "_critic"))
		self.critic_optimizer.load_state_dict(torch.load(filename + "_critic_optimizer"))
		self.critic_target = copy.deepcopy(self.critic)

		self.actor.load_state_dict(torch.load(filename + "_actor"))
		self.actor_optimizer.load_state_dict(torch.load(filename + "_actor_optimizer"))
		self.actor_target = copy.deepcopy(self.actor)