update projects

projects/codes/TD3/agent.py

@@ -0,0 +1,177 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+Author: JiangJi
+Email: johnjim0816@gmail.com
+Date: 2021-12-22 10:40:05
+LastEditor: JiangJi
+LastEditTime: 2021-12-22 10:43:55
+Discription: 
+'''
+import copy
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from TD3.memory import ReplayBuffer
+
+class Actor(nn.Module):
+	
+	def __init__(self, input_dim, output_dim, max_action):
+		'''[summary]
+
+		Args:
+			input_dim (int): 输入维度，这里等于n_states
+			output_dim (int): 输出维度，这里等于n_actions
+			max_action (int): action的最大值
+		'''		
+		super(Actor, self).__init__()
+
+		self.l1 = nn.Linear(input_dim, 256)
+		self.l2 = nn.Linear(256, 256)
+		self.l3 = nn.Linear(256, output_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, input_dim, output_dim):
+		super(Critic, self).__init__()
+
+		# Q1 architecture
+		self.l1 = nn.Linear(input_dim + output_dim, 256)
+		self.l2 = nn.Linear(256, 256)
+		self.l3 = nn.Linear(256, 1)
+
+		# Q2 architecture
+		self.l4 = nn.Linear(input_dim + output_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,
+		input_dim,
+		output_dim,
+		max_action,
+		cfg,
+	):
+		self.max_action = max_action
+		self.gamma = cfg.gamma
+		self.lr = cfg.lr
+		self.policy_noise = cfg.policy_noise
+		self.noise_clip = cfg.noise_clip
+		self.policy_freq = cfg.policy_freq
+		self.batch_size =  cfg.batch_size 
+		self.device = cfg.device
+		self.total_it = 0
+
+		self.actor = Actor(input_dim, output_dim, max_action).to(self.device)
+		self.actor_target = copy.deepcopy(self.actor)
+		self.actor_optimizer = torch.optim.Adam(self.actor.parameters(), lr=3e-4)
+
+		self.critic = Critic(input_dim, output_dim).to(self.device)
+		self.critic_target = copy.deepcopy(self.critic)
+		self.critic_optimizer = torch.optim.Adam(self.critic.parameters(), lr=3e-4)
+		self.memory = ReplayBuffer(input_dim, output_dim)
+
+	def choose_action(self, state):
+		state = torch.FloatTensor(state.reshape(1, -1)).to(self.device)
+		return self.actor(state).cpu().data.numpy().flatten()
+
+	def update(self):
+		self.total_it += 1
+
+		# Sample replay buffer 
+		state, action, next_state, reward, not_done = self.memory.sample(self.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.gamma * 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.lr * param.data + (1 - self.lr) * target_param.data)
+
+			for param, target_param in zip(self.actor.parameters(), self.actor_target.parameters()):
+				target_param.data.copy_(self.lr * param.data + (1 - self.lr) * target_param.data)
+
+
+	def save(self, path):
+		torch.save(self.critic.state_dict(), path + "td3_critic")
+		torch.save(self.critic_optimizer.state_dict(), path + "td3_critic_optimizer")
+		
+		torch.save(self.actor.state_dict(), path + "td3_actor")
+		torch.save(self.actor_optimizer.state_dict(), path + "td3_actor_optimizer")
+
+
+	def load(self, path):
+		self.critic.load_state_dict(torch.load(path + "td3_critic"))
+		self.critic_optimizer.load_state_dict(torch.load(path + "td3_critic_optimizer"))
+		self.critic_target = copy.deepcopy(self.critic)
+
+		self.actor.load_state_dict(torch.load(path + "td3_actor"))
+		self.actor_optimizer.load_state_dict(torch.load(path + "td3_actor_optimizer"))
+		self.actor_target = copy.deepcopy(self.actor)
+