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SAC.py
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SAC.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal
import numpy as np
import copy
class Actor(nn.Module):
def __init__(self, state_dim, action_dim, hidden_width, max_action):
super(Actor, self).__init__()
self.max_action = max_action
self.l1 = nn.Linear(state_dim, hidden_width)
self.l2 = nn.Linear(hidden_width, hidden_width)
self.mean_layer = nn.Linear(hidden_width, action_dim)
self.log_std_layer = nn.Linear(hidden_width, action_dim)
def forward(self, x, deterministic=False, with_logprob=True):
x = F.relu(self.l1(x))
x = F.relu(self.l2(x))
mean = self.mean_layer(x)
log_std = self.log_std_layer(x) # We output the log_std to ensure that std=exp(log_std)>0
log_std = torch.clamp(log_std, -20, 2)
std = torch.exp(log_std)
dist = Normal(mean, std) # Generate a Gaussian distribution
if deterministic: # When evaluating,we use the deterministic policy
a = mean
else:
a = dist.rsample() # reparameterization trick: mean+std*N(0,1)
if with_logprob: # The method refers to Open AI Spinning up, which is more stable.
log_pi = dist.log_prob(a).sum(dim=1, keepdim=True)
log_pi -= (2 * (np.log(2) - a - F.softplus(-2 * a))).sum(dim=1, keepdim=True)
else:
log_pi = None
for i in range(len(a)):
a[i] = torch.tensor(self.max_action) * torch.tanh(a[i])
return a, log_pi
class Critic(nn.Module): # According to (s,a), directly calculate Q(s,a)
def __init__(self, state_dim, action_dim, hidden_width):
super(Critic, self).__init__()
# Q1
self.l1 = nn.Linear(state_dim + action_dim, hidden_width)
self.l2 = nn.Linear(hidden_width, hidden_width)
self.l3 = nn.Linear(hidden_width, 1)
# Q2
self.l4 = nn.Linear(state_dim + action_dim, hidden_width)
self.l5 = nn.Linear(hidden_width, hidden_width)
self.l6 = nn.Linear(hidden_width, 1)
def forward(self, s, a):
s_a = torch.cat([s, a], 1)
q1 = F.relu(self.l1(s_a))
q1 = F.relu(self.l2(q1))
q1 = self.l3(q1)
q2 = F.relu(self.l4(s_a))
q2 = F.relu(self.l5(q2))
q2 = self.l6(q2)
return q1, q2
class SAC(object):
def __init__(self, state_dim, action_dim, max_action, args=None):
self.max_action = max_action
self.hidden_width = 256 # The number of neurons in hidden layers of the neural network
if args:
self.batch_size = args.batchsize # batch size
self.GAMMA = args.gamma # discount factor
self.TAU = args.tau # Softly update the target network
self.q_lr = args.q_lr # learning rate
self.policy_lr = args.policy_lr
self.adaptive_alpha = args.adaptive_alpha # Whether to automatically learn the temperature alpha
self.policy_frequency = args.policy_frequency
self.target_network_frequency = args.target_network_frequency
if self.adaptive_alpha:
# Target Entropy = −dim(A) (e.g. , -6 for HalfCheetah-v2) as given in the paper
self.target_entropy = -action_dim
# We learn log_alpha instead of alpha to ensure that alpha=exp(log_alpha)>0
self.log_alpha = torch.zeros(1, requires_grad=True)
self.alpha = self.log_alpha.exp().item()
self.alpha_optimizer = torch.optim.Adam([self.log_alpha], lr=self.q_lr)
else:
self.alpha = args.alpha
else:
self.policy_lr = 0
self.q_lr = 0
'''1. Input: initial policy parameters theta, Q-function parameters phi_1, phi_2'''
self.actor = Actor(state_dim, action_dim, self.hidden_width, max_action)
self.critic = Critic(state_dim, action_dim, self.hidden_width)
'''2: Set target parameters equal to main parameters phi_targ1 <- phi_1; phi_targ2 <- phi_2'''
self.critic_target = copy.deepcopy(self.critic)
self.actor_optimizer = torch.optim.Adam(self.actor.parameters(), lr=self.policy_lr)
self.critic_optimizer = torch.optim.Adam(self.critic.parameters(), lr=self.q_lr)
def choose_action(self, s, deterministic=False):
s = torch.unsqueeze(torch.tensor(s, dtype=torch.float), 0)
a, _ = self.actor(s, deterministic, False) # When choosing actions, we do not need to compute log_pi
return a.data.numpy().flatten()
def learn(self, relay_buffer, total_steps):
'''11. Randomly sample a batch of transitions, B =(s,a, r, s',d) from D'''
batch_s, batch_a, batch_r, batch_s_, batch_d = relay_buffer.sample(self.batch_size) # Sample a batch
'''12. Compute targets for the Q functions:'''
with torch.no_grad():
batch_a_, log_pi_ = self.actor(batch_s_) # a' from the current policy
# Compute target Q
target_Q1, target_Q2 = self.critic_target(batch_s_, batch_a_)
'''在下一步取了min'''
target_Q = batch_r + self.GAMMA * (1 - batch_d) * (torch.min(target_Q1, target_Q2) - self.alpha * log_pi_)
'''
13. Update Q-functions by one step of gradient descent using :
critic_loss = F.mse_loss(current_Q1, target_Q) + F.mse_loss(current_Q2, target_Q)
'''
# Compute current Q
current_Q1, current_Q2 = self.critic(batch_s, batch_a)
# 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()
# Freeze critic networks so you don't waste computational effort
for params in self.critic.parameters():
params.requires_grad = False
# Compute actor loss
'''
14. Update policy by one step of gradient ascent using:
actor_loss = (self.alpha * log_pi - Q).mean()
p.s. and update the alpha if it's asked to do so.
'''
if total_steps % self.policy_frequency == 0:
for _ in range(self.policy_frequency):
'''从这里开始更新actor'''
a, log_pi = self.actor(batch_s)
Q1, Q2 = self.critic(batch_s, a)
Q = torch.min(Q1, Q2)
actor_loss = (self.alpha * log_pi - Q).mean()
self.actor_optimizer.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
# Update alpha
if self.adaptive_alpha:
# We learn log_alpha instead of alpha to ensure that alpha=exp(log_alpha)>0
alpha_loss = -(self.log_alpha.exp() * (log_pi + self.target_entropy).detach()).mean()
self.alpha_optimizer.zero_grad()
alpha_loss.backward()
self.alpha_optimizer.step()
self.alpha = self.log_alpha.exp()
# Unfreeze critic networks
for params in self.critic.parameters():
params.requires_grad = True
# Softly update target networks
'''
15. Update target networks with
'''
if total_steps % self.target_network_frequency == 0:
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)
def save(self, filename):
torch.save(self.critic.state_dict(), filename + "_critic.pth")
torch.save(self.critic_optimizer.state_dict(), filename + "_critic_optimizer.pth")
torch.save(self.actor.state_dict(), filename + "_actor.pth")
torch.save(self.actor_optimizer.state_dict(), filename + "_actor_optimizer.pth")
#优化器参数不用存
def load(self, filename):
self.critic.load_state_dict(torch.load(filename + "_critic.pth"))
self.critic_optimizer.load_state_dict(torch.load(filename + "_critic_optimizer.pth"))
self.critic_target = copy.deepcopy(self.critic)
self.actor.load_state_dict(torch.load(filename + "_actor.pth"))
self.actor_optimizer.load_state_dict(torch.load(filename + "_actor_optimizer.pth"))