e0842e71dc
* v0.5 (#9) * update idql configs * update awr configs * update dipo configs * update qsm configs * update dqm configs * update project version to 0.5.0
89 lines
2.4 KiB
Python
89 lines
2.4 KiB
Python
"""
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Soft Actor Critic (SAC) with Gaussian policy.
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"""
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import torch
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import logging
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from copy import deepcopy
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import torch.nn.functional as F
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from model.common.gaussian import GaussianModel
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log = logging.getLogger(__name__)
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class SAC_Gaussian(GaussianModel):
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def __init__(
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self,
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actor,
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critic,
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**kwargs,
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):
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super().__init__(network=actor, **kwargs)
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# initialize doubel critic networks
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self.critic = critic.to(self.device)
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# initialize double target networks
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self.target_critic = deepcopy(self.critic).to(self.device)
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def loss_critic(
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self,
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obs,
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next_obs,
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actions,
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rewards,
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terminated,
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gamma,
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alpha,
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):
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with torch.no_grad():
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next_actions, next_logprobs = self.forward(
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cond=next_obs,
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deterministic=False,
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get_logprob=True,
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)
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next_q1, next_q2 = self.target_critic(
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next_obs,
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next_actions,
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)
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next_q = torch.min(next_q1, next_q2) - alpha * next_logprobs
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# target value
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target_q = rewards + gamma * next_q * (1 - terminated)
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current_q1, current_q2 = self.critic(obs, actions)
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loss_critic = F.mse_loss(current_q1, target_q) + F.mse_loss(
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current_q2, target_q
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)
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return loss_critic
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def loss_actor(self, obs, alpha):
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action, logprob = self.forward(
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obs,
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deterministic=False,
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reparameterize=True,
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get_logprob=True,
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)
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current_q1, current_q2 = self.critic(obs, action)
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loss_actor = -torch.min(current_q1, current_q2) + alpha * logprob
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return loss_actor.mean()
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def loss_temperature(self, obs, alpha, target_entropy):
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with torch.no_grad():
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_, logprob = self.forward(
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obs,
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deterministic=False,
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get_logprob=True,
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)
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loss_alpha = -torch.mean(alpha * (logprob + target_entropy))
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return loss_alpha
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def update_target_critic(self, tau):
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for target_param, source_param in zip(
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self.target_critic.parameters(), self.critic.parameters()
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):
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target_param.data.copy_(
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target_param.data * (1.0 - tau) + source_param.data * tau
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)
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