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DATD3.py
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import copy
import numpy as np
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
class Actor(nn.Module):
def __init__(self, state_dim, action_dim, max_action, hidden_sizes=[400, 300]):
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=[400, 300]):
super(Critic, self).__init__()
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)
def forward(self, state, action):
if len(state.shape) == 3:
sa = torch.cat([state, action], 2)
else:
sa = torch.cat([state, action], 1)
q = F.relu(self.l1(sa))
q = F.relu(self.l2(q))
q = self.l3(q)
return q
class DATD3(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,
actor_lr=1e-3,
critic_lr=1e-3,
hidden_sizes=[400, 300],
):
self.device = device
self.actor1 = Actor(state_dim, action_dim, max_action, hidden_sizes).to(self.device)
self.actor1_target = copy.deepcopy(self.actor1)
self.actor1_optimizer = torch.optim.Adam(self.actor1.parameters(), lr=actor_lr)
self.actor2 = Actor(state_dim, action_dim, max_action, hidden_sizes).to(self.device)
self.actor2_target = copy.deepcopy(self.actor2)
self.actor2_optimizer = torch.optim.Adam(self.actor2.parameters(), lr=actor_lr)
self.critic1 = Critic(state_dim, action_dim, hidden_sizes).to(self.device)
self.critic1_target = copy.deepcopy(self.critic1)
self.critic1_optimizer = torch.optim.Adam(self.critic1.parameters(), lr=critic_lr)
self.critic2 = Critic(state_dim, action_dim, hidden_sizes).to(self.device)
self.critic2_target = copy.deepcopy(self.critic2)
self.critic2_optimizer = torch.optim.Adam(self.critic2.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
def select_action(self, state):
state = torch.FloatTensor(state.reshape(1, -1)).to(self.device)
action1 = self.actor1(state)
action2 = self.actor2(state)
q1 = self.critic1(state, action1)
q2 = self.critic2(state, action2)
action = action1 if q1 >= q2 else action2
return action.cpu().data.numpy().flatten()
def train(self, replay_buffer, batch_size=100):
## cross update scheme
self.train_one_q_and_pi(replay_buffer, True, batch_size=batch_size)
self.train_one_q_and_pi(replay_buffer, False, batch_size=batch_size)
def train_one_q_and_pi(self, replay_buffer, update_a1 = True, batch_size=100):
state, action, next_state, reward, not_done = replay_buffer.sample(batch_size)
with torch.no_grad():
next_action1 = self.actor1_target(next_state)
next_action2 = self.actor2_target(next_state)
noise = torch.randn(
(action.shape[0], action.shape[1]),
dtype=action.dtype, layout=action.layout, device=action.device
) * self.policy_noise
noise = noise.clamp(-self.noise_clip, self.noise_clip)
next_action1 = (next_action1 + noise).clamp(-self.max_action, self.max_action)
next_action2 = (next_action2 + noise).clamp(-self.max_action, self.max_action)
next_Q1_a1 = self.critic1_target(next_state, next_action1)
next_Q2_a1 = self.critic2_target(next_state, next_action1)
next_Q1_a2 = self.critic1_target(next_state, next_action2)
next_Q2_a2 = self.critic2_target(next_state, next_action2)
## min first, max afterward to avoid underestimation bias
next_Q1 = torch.min(next_Q1_a1, next_Q2_a1)
next_Q2 = torch.min(next_Q1_a2, next_Q2_a2)
next_Q = torch.max(next_Q1, next_Q2)
target_Q = reward + not_done * self.discount * next_Q
if update_a1:
current_Q1 = self.critic1(state, action)
critic1_loss = F.mse_loss(current_Q1, target_Q)
self.critic1_optimizer.zero_grad()
critic1_loss.backward()
self.critic1_optimizer.step()
actor1_loss = -self.critic1(state, self.actor1(state)).mean()
self.actor1_optimizer.zero_grad()
actor1_loss.backward()
self.actor1_optimizer.step()
for param, target_param in zip(self.critic1.parameters(), self.critic1_target.parameters()):
target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)
for param, target_param in zip(self.actor1.parameters(), self.actor1_target.parameters()):
target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)
else:
current_Q2 = self.critic2(state, action)
critic2_loss = F.mse_loss(current_Q2, target_Q)
self.critic2_optimizer.zero_grad()
critic2_loss.backward()
self.critic2_optimizer.step()
actor2_loss = -self.critic2(state, self.actor2(state)).mean()
self.actor2_optimizer.zero_grad()
actor2_loss.backward()
self.actor2_optimizer.step()
for param, target_param in zip(self.critic2.parameters(), self.critic2_target.parameters()):
target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)
for param, target_param in zip(self.actor2.parameters(), self.actor2_target.parameters()):
target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)
def save(self, filename):
torch.save(self.critic1.state_dict(), filename + "_critic1")
torch.save(self.critic1_optimizer.state_dict(), filename + "_critic1_optimizer")
torch.save(self.actor1.state_dict(), filename + "_actor1")
torch.save(self.actor1_optimizer.state_dict(), filename + "_actor1_optimizer")
torch.save(self.critic2.state_dict(), filename + "_critic2")
torch.save(self.critic2_optimizer.state_dict(), filename + "_critic2_optimizer")
torch.save(self.actor2.state_dict(), filename + "_actor2")
torch.save(self.actor2_optimizer.state_dict(), filename + "_actor2_optimizer")
def load(self, filename):
self.critic1.load_state_dict(torch.load(filename + "_critic1"))
self.critic1_optimizer.load_state_dict(torch.load(filename + "_critic1_optimizer"))
self.actor1.load_state_dict(torch.load(filename + "_actor1"))
self.actor1_optimizer.load_state_dict(torch.load(filename + "_actor1_optimizer"))
self.critic2.load_state_dict(torch.load(filename + "_critic2"))
self.critic2_optimizer.load_state_dict(torch.load(filename + "_critic2_optimizer"))
self.actor2.load_state_dict(torch.load(filename + "_actor2"))
self.actor2_optimizer.load_state_dict(torch.load(filename + "_actor2_optimizer"))