text: import copy
  import numpy as np
  import utils
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  # 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, hidden_sizes=[256, 256]):
  		super(Actor, self).__init__()
  
  		self.l1 = nn.Linear(state_dim, hidden_sizes[0])
  		self.l2 = nn.Linear(hidden_sizes[0], hidden_sizes[1])
  		self.l3 = nn.Linear(hidden_sizes[1], 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, hidden_sizes=[256, 256]):
  		super(Critic, self).__init__()
  
  		# Q1 architecture
  		self.l1 = nn.Linear(state_dim + action_dim, hidden_sizes[0])
  		self.l2 = nn.Linear(hidden_sizes[0], hidden_sizes[1])
  		self.l3 = nn.Linear(hidden_sizes[1], 1)
  
  		# Q2 architecture
  		self.l4 = nn.Linear(state_dim + action_dim, hidden_sizes[0])
  		self.l5 = nn.Linear(hidden_sizes[0], hidden_sizes[1])
  		self.l6 = nn.Linear(hidden_sizes[1], 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,
  		device,
  		discount=0.99,
  		tau=0.005,
  		policy_noise=0.2,
  		noise_clip=0.5,
  		policy_freq=2,
  		actor_lr=1e-3,
  		critic_lr=1e-3,
  		hidden_sizes=[400, 300],
  	):
  		self.device = device
  
  		self.actor = Actor(state_dim, action_dim, max_action, hidden_sizes).to(self.device)
  		self.actor_target = copy.deepcopy(self.actor)
  		self.actor_optimizer = torch.optim.Adam(self.actor.parameters(), lr=actor_lr)
  
  		self.critic = Critic(state_dim, action_dim, hidden_sizes).to(self.device)
  		self.critic_target = copy.deepcopy(self.critic)
  		self.critic_optimizer = torch.optim.Adam(self.critic.parameters(), lr=critic_lr)
  
  		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(self.device)
  		action = self.actor(state)
  		return action.cpu().data.numpy().flatten()
  
  
  	def train(self, replay_buffer, batch_size=100):
  		self.total_it += 1
  
  		# Sample replay buffer 
  		state, action, next_state, reward, not_done = replay_buffer.sample(batch_size)
  
  		with torch.no_grad():
  			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)
  
  			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
  
  		current_Q1, current_Q2 = self.critic(state, action)
  
  		critic_loss = F.mse_loss(current_Q1, target_Q) + F.mse_loss(current_Q2, target_Q)
  	
  		self.critic_optimizer.zero_grad()
  		critic_loss.backward()
  		self.critic_optimizer.step()
  
  		if self.total_it % self.policy_freq == 0:
  			actor_loss = -self.critic.Q1(state, self.actor(state)).mean()
  			
  			self.actor_optimizer.zero_grad()
  			actor_loss.backward()
  			self.actor_optimizer.step()
  
  			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.actor.load_state_dict(torch.load(filename + "_actor"))
  		self.actor_optimizer.load_state_dict(torch.load(filename + "_actor_optimizer"))
  