from typing import Any, Dict, Optional, Tuple, Union
import numpy as np
import torch
from torch.distributions import Categorical
from tianshou.data import Batch, ReplayBuffer, to_torch
from tianshou.policy import SACPolicy
[docs]class DiscreteSACPolicy(SACPolicy):
"""Implementation of SAC for Discrete Action Settings. arXiv:1910.07207.
:param torch.nn.Module actor: the actor network following the rules in
:class:`~tianshou.policy.BasePolicy`. (s -> logits)
:param torch.optim.Optimizer actor_optim: the optimizer for actor network.
:param torch.nn.Module critic1: the first critic network. (s -> Q(s))
:param torch.optim.Optimizer critic1_optim: the optimizer for the first
critic network.
:param torch.nn.Module critic2: the second critic network. (s -> Q(s))
:param torch.optim.Optimizer critic2_optim: the optimizer for the second
critic network.
:param float tau: param for soft update of the target network. Default to 0.005.
:param float gamma: discount factor, in [0, 1]. Default to 0.99.
:param (float, torch.Tensor, torch.optim.Optimizer) or float alpha: entropy
regularization coefficient. Default to 0.2.
If a tuple (target_entropy, log_alpha, alpha_optim) is provided, the
alpha is automatically tuned.
:param bool reward_normalization: normalize the reward to Normal(0, 1).
Default to False.
:param lr_scheduler: a learning rate scheduler that adjusts the learning rate in
optimizer in each policy.update(). Default to None (no lr_scheduler).
.. seealso::
Please refer to :class:`~tianshou.policy.BasePolicy` for more detailed
explanation.
"""
def __init__(
self,
actor: torch.nn.Module,
actor_optim: torch.optim.Optimizer,
critic1: torch.nn.Module,
critic1_optim: torch.optim.Optimizer,
critic2: torch.nn.Module,
critic2_optim: torch.optim.Optimizer,
tau: float = 0.005,
gamma: float = 0.99,
alpha: Union[float, Tuple[float, torch.Tensor, torch.optim.Optimizer]] = 0.2,
reward_normalization: bool = False,
estimation_step: int = 1,
**kwargs: Any,
) -> None:
super().__init__(
actor,
actor_optim,
critic1,
critic1_optim,
critic2,
critic2_optim,
tau,
gamma,
alpha,
reward_normalization,
estimation_step,
action_scaling=False,
action_bound_method="",
**kwargs
)
self._alpha: Union[float, torch.Tensor]
[docs] def forward( # type: ignore
self,
batch: Batch,
state: Optional[Union[dict, Batch, np.ndarray]] = None,
input: str = "obs",
**kwargs: Any,
) -> Batch:
obs = batch[input]
logits, hidden = self.actor(obs, state=state, info=batch.info)
dist = Categorical(logits=logits)
if self._deterministic_eval and not self.training:
act = logits.argmax(axis=-1)
else:
act = dist.sample()
return Batch(logits=logits, act=act, state=hidden, dist=dist)
def _target_q(self, buffer: ReplayBuffer, indices: np.ndarray) -> torch.Tensor:
batch = buffer[indices] # batch.obs: s_{t+n}
obs_next_result = self(batch, input="obs_next")
dist = obs_next_result.dist
target_q = dist.probs * torch.min(
self.critic1_old(batch.obs_next),
self.critic2_old(batch.obs_next),
)
target_q = target_q.sum(dim=-1) + self._alpha * dist.entropy()
return target_q
[docs] def learn(self, batch: Batch, **kwargs: Any) -> Dict[str, float]:
weight = batch.pop("weight", 1.0)
target_q = batch.returns.flatten()
act = to_torch(
batch.act[:, np.newaxis], device=target_q.device, dtype=torch.long
)
# critic 1
current_q1 = self.critic1(batch.obs).gather(1, act).flatten()
td1 = current_q1 - target_q
critic1_loss = (td1.pow(2) * weight).mean()
self.critic1_optim.zero_grad()
critic1_loss.backward()
self.critic1_optim.step()
# critic 2
current_q2 = self.critic2(batch.obs).gather(1, act).flatten()
td2 = current_q2 - target_q
critic2_loss = (td2.pow(2) * weight).mean()
self.critic2_optim.zero_grad()
critic2_loss.backward()
self.critic2_optim.step()
batch.weight = (td1 + td2) / 2.0 # prio-buffer
# actor
dist = self(batch).dist
entropy = dist.entropy()
with torch.no_grad():
current_q1a = self.critic1(batch.obs)
current_q2a = self.critic2(batch.obs)
q = torch.min(current_q1a, current_q2a)
actor_loss = -(self._alpha * entropy + (dist.probs * q).sum(dim=-1)).mean()
self.actor_optim.zero_grad()
actor_loss.backward()
self.actor_optim.step()
if self._is_auto_alpha:
log_prob = -entropy.detach() + self._target_entropy
alpha_loss = -(self._log_alpha * log_prob).mean()
self._alpha_optim.zero_grad()
alpha_loss.backward()
self._alpha_optim.step()
self._alpha = self._log_alpha.detach().exp()
self.sync_weight()
result = {
"loss/actor": actor_loss.item(),
"loss/critic1": critic1_loss.item(),
"loss/critic2": critic2_loss.item(),
}
if self._is_auto_alpha:
result["loss/alpha"] = alpha_loss.item()
result["alpha"] = self._alpha.item() # type: ignore
return result
[docs] def exploration_noise(self, act: Union[np.ndarray, Batch],
batch: Batch) -> Union[np.ndarray, Batch]:
return act