107 lines
4.2 KiB
Python
107 lines
4.2 KiB
Python
import torch
|
|
import numpy as np
|
|
from copy import deepcopy
|
|
import torch.nn.functional as F
|
|
|
|
from tianshou.data import Batch
|
|
from tianshou.policy import DDPGPolicy
|
|
|
|
|
|
class SACPolicy(DDPGPolicy):
|
|
"""docstring for SACPolicy"""
|
|
|
|
def __init__(self, actor, actor_optim, critic1, critic1_optim,
|
|
critic2, critic2_optim, tau=0.005, gamma=0.99,
|
|
alpha=0.2, action_range=None, reward_normalization=True):
|
|
super().__init__(None, None, None, None, tau, gamma, 0,
|
|
action_range, reward_normalization)
|
|
self.actor, self.actor_optim = actor, actor_optim
|
|
self.critic1, self.critic1_old = critic1, deepcopy(critic1)
|
|
self.critic1_old.eval()
|
|
self.critic1_optim = critic1_optim
|
|
self.critic2, self.critic2_old = critic2, deepcopy(critic2)
|
|
self.critic2_old.eval()
|
|
self.critic2_optim = critic2_optim
|
|
self._alpha = alpha
|
|
self.__eps = np.finfo(np.float32).eps.item()
|
|
|
|
def train(self):
|
|
self.training = True
|
|
self.actor.train()
|
|
self.critic1.train()
|
|
self.critic2.train()
|
|
|
|
def eval(self):
|
|
self.training = False
|
|
self.actor.eval()
|
|
self.critic1.eval()
|
|
self.critic2.eval()
|
|
|
|
def sync_weight(self):
|
|
for o, n in zip(
|
|
self.critic1_old.parameters(), self.critic1.parameters()):
|
|
o.data.copy_(o.data * (1 - self._tau) + n.data * self._tau)
|
|
for o, n in zip(
|
|
self.critic2_old.parameters(), self.critic2.parameters()):
|
|
o.data.copy_(o.data * (1 - self._tau) + n.data * self._tau)
|
|
|
|
def __call__(self, batch, state=None, input='obs'):
|
|
obs = getattr(batch, input)
|
|
logits, h = self.actor(obs, state=state, info=batch.info)
|
|
assert isinstance(logits, tuple)
|
|
dist = torch.distributions.Normal(*logits)
|
|
|
|
x = dist.rsample()
|
|
y = torch.tanh(x)
|
|
act = y * self._action_scale + self._action_bias
|
|
log_prob = dist.log_prob(x) - torch.log(
|
|
self._action_scale * (1 - y.pow(2)) + self.__eps)
|
|
act = act.clamp(self._range[0], self._range[1])
|
|
return Batch(
|
|
logits=logits, act=act, state=h, dist=dist, log_prob=log_prob)
|
|
|
|
def learn(self, batch, batch_size=None, repeat=1):
|
|
obs_next_result = self(batch, input='obs_next')
|
|
a_ = obs_next_result.act
|
|
dev = a_.device
|
|
batch.act = torch.tensor(batch.act, dtype=torch.float, device=dev)
|
|
target_q = torch.min(
|
|
self.critic1_old(batch.obs_next, a_),
|
|
self.critic2_old(batch.obs_next, a_),
|
|
) - self._alpha * obs_next_result.log_prob
|
|
rew = torch.tensor(batch.rew, dtype=torch.float, device=dev)[:, None]
|
|
if self._rew_norm:
|
|
rew = (rew - self._rew_mean) / (self._rew_std + self.__eps)
|
|
done = torch.tensor(batch.done, dtype=torch.float, device=dev)[:, None]
|
|
target_q = rew + ((1. - done) * self._gamma * target_q).detach()
|
|
obs_result = self(batch)
|
|
a = obs_result.act
|
|
current_q1, current_q1a = self.critic1(
|
|
np.concatenate([batch.obs, batch.obs]), torch.cat([batch.act, a])
|
|
).split(batch.obs.shape[0])
|
|
current_q2, current_q2a = self.critic2(
|
|
np.concatenate([batch.obs, batch.obs]), torch.cat([batch.act, a])
|
|
).split(batch.obs.shape[0])
|
|
actor_loss = (self._alpha * obs_result.log_prob - torch.min(
|
|
current_q1a, current_q2a)).mean()
|
|
# critic 1
|
|
critic1_loss = F.mse_loss(current_q1, target_q)
|
|
self.critic1_optim.zero_grad()
|
|
critic1_loss.backward(retain_graph=True)
|
|
self.critic1_optim.step()
|
|
# critic 2
|
|
critic2_loss = F.mse_loss(current_q2, target_q)
|
|
self.critic2_optim.zero_grad()
|
|
critic2_loss.backward(retain_graph=True)
|
|
self.critic2_optim.step()
|
|
# actor
|
|
self.actor_optim.zero_grad()
|
|
actor_loss.backward()
|
|
self.actor_optim.step()
|
|
self.sync_weight()
|
|
return {
|
|
'loss/actor': actor_loss.detach().cpu().numpy(),
|
|
'loss/critic1': critic1_loss.detach().cpu().numpy(),
|
|
'loss/critic2': critic2_loss.detach().cpu().numpy(),
|
|
}
|