mirror of
https://github.com/wassname/pytorch-soft-actor-critic.git
synced 2026-06-27 18:06:10 +08:00
wassname
121 lines
5.4 KiB
Python
121 lines
5.4 KiB
Python
import os
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import torch
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import torch.nn.functional as F
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from torch.optim import Adam
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from utils import soft_update, hard_update
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from model import GaussianPolicy, QNetwork, DeterministicPolicy
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from loguru import logger
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class SAC(object):
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def __init__(self, num_inputs, action_space, args):
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self.gamma = args.gamma
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self.tau = args.tau
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self.alpha = args.alpha
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self.policy_type = args.policy
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self.target_update_interval = args.target_update_interval
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self.automatic_entropy_tuning = args.automatic_entropy_tuning
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self.device = torch.device("cuda" if args.cuda else "cpu")
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self.critic = QNetwork(num_inputs, action_space.shape[0], args.hidden_size).to(device=self.device)
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self.critic_optim = Adam(self.critic.parameters(), lr=args.lr)
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self.critic_target = QNetwork(num_inputs, action_space.shape[0], args.hidden_size).to(self.device)
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hard_update(self.critic_target, self.critic)
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if self.policy_type == "Gaussian":
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# Target Entropy = −dim(A) (e.g. , -6 for HalfCheetah-v2) as given in the paper
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if self.automatic_entropy_tuning is True:
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self.target_entropy = -torch.prod(torch.Tensor(action_space.shape).to(self.device)).item()
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self.log_alpha = torch.zeros(1, requires_grad=True, device=self.device)
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self.alpha_optim = Adam([self.log_alpha], lr=args.lr)
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self.policy = GaussianPolicy(num_inputs, action_space.shape[0], args.hidden_size, action_space).to(self.device)
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self.policy_optim = Adam(self.policy.parameters(), lr=args.lr)
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else:
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self.alpha = 0
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self.automatic_entropy_tuning = False
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self.policy = DeterministicPolicy(num_inputs, action_space.shape[0], args.hidden_size, action_space).to(self.device)
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self.policy_optim = Adam(self.policy.parameters(), lr=args.lr)
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def select_action(self, state, evaluate=False):
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state = torch.FloatTensor(state).to(self.device).unsqueeze(0)
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if evaluate is False:
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action, _, _ = self.policy.sample(state)
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else:
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_, _, action = self.policy.sample(state)
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return action.detach().cpu().numpy()[0]
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def update_parameters(self, memory, batch_size, updates):
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# Sample a batch from memory
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state_batch, action_batch, reward_batch, next_state_batch, mask_batch = memory.sample(batch_size=batch_size)
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state_batch = torch.FloatTensor(state_batch).to(self.device)
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next_state_batch = torch.FloatTensor(next_state_batch).to(self.device)
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action_batch = torch.FloatTensor(action_batch).to(self.device)
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reward_batch = torch.FloatTensor(reward_batch).to(self.device).unsqueeze(1)
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mask_batch = torch.FloatTensor(mask_batch).to(self.device).unsqueeze(1)
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with torch.no_grad():
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next_state_action, next_state_log_pi, _ = self.policy.sample(next_state_batch)
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qf1_next_target, qf2_next_target = self.critic_target(next_state_batch, next_state_action)
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min_qf_next_target = torch.min(qf1_next_target, qf2_next_target) - self.alpha * next_state_log_pi
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next_q_value = reward_batch + mask_batch * self.gamma * (min_qf_next_target)
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qf1, qf2 = self.critic(state_batch, action_batch) # Two Q-functions to mitigate positive bias in the policy improvement step
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qf1_loss = F.mse_loss(qf1, next_q_value) # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
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qf2_loss = F.mse_loss(qf2, next_q_value) # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
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qf_loss = qf1_loss + qf2_loss
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self.critic_optim.zero_grad()
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qf_loss.backward()
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self.critic_optim.step()
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pi, log_pi, _ = self.policy.sample(state_batch)
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qf1_pi, qf2_pi = self.critic(state_batch, pi)
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min_qf_pi = torch.min(qf1_pi, qf2_pi)
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policy_loss = ((self.alpha * log_pi) - min_qf_pi).mean() # Jπ = 𝔼st∼D,εt∼N[α * logπ(f(εt;st)|st) − Q(st,f(εt;st))]
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self.policy_optim.zero_grad()
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policy_loss.backward()
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self.policy_optim.step()
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if self.automatic_entropy_tuning:
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alpha_loss = -(self.log_alpha * (log_pi + self.target_entropy).detach()).mean()
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self.alpha_optim.zero_grad()
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alpha_loss.backward()
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self.alpha_optim.step()
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self.alpha = self.log_alpha.exp()
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alpha_tlogs = self.alpha.clone() # For TensorboardX logs
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else:
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alpha_loss = torch.tensor(0.).to(self.device)
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alpha_tlogs = torch.tensor(self.alpha) # For TensorboardX logs
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if updates % self.target_update_interval == 0:
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soft_update(self.critic_target, self.critic, self.tau)
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return qf1_loss.item(), qf2_loss.item(), policy_loss.item(), alpha_loss.item(), alpha_tlogs.item()
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# Save model parameters
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def save_model(self, actor_path=None, critic_path=None):
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logger.debug(f'saving models to {actor_path} and {critic_path}'))
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torch.save(self.policy.state_dict(), actor_path)
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torch.save(self.critic.state_dict(), critic_path)
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# Load model parameters
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def load_model(self, actor_path, critic_path):
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logger.info(f'Loading models from {actor_path} and {critic_path}'))
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if actor_path is not None:
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self.policy.load_state_dict(torch.load(actor_path))
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if critic_path is not None:
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self.critic.load_state_dict(torch.load(critic_path))
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