Source code for tianshou.policy.modelfree.a2c

from typing import Any, Dict, List, Optional, Type

import numpy as np
import torch
import torch.nn.functional as F
from torch import nn

from import Batch, ReplayBuffer, to_torch_as
from tianshou.policy import PGPolicy
from import ActorCritic

[docs]class A2CPolicy(PGPolicy): """Implementation of Synchronous Advantage Actor-Critic. arXiv:1602.01783. :param torch.nn.Module actor: the actor network following the rules in :class:`~tianshou.policy.BasePolicy`. (s -> logits) :param torch.nn.Module critic: the critic network. (s -> V(s)) :param torch.optim.Optimizer optim: the optimizer for actor and critic network. :param dist_fn: distribution class for computing the action. :type dist_fn: Type[torch.distributions.Distribution] :param float discount_factor: in [0, 1]. Default to 0.99. :param float vf_coef: weight for value loss. Default to 0.5. :param float ent_coef: weight for entropy loss. Default to 0.01. :param float max_grad_norm: clipping gradients in back propagation. Default to None. :param float gae_lambda: in [0, 1], param for Generalized Advantage Estimation. Default to 0.95. :param bool reward_normalization: normalize estimated values to have std close to 1. Default to False. :param int max_batchsize: the maximum size of the batch when computing GAE, depends on the size of available memory and the memory cost of the model; should be as large as possible within the memory constraint. Default to 256. :param bool action_scaling: whether to map actions from range [-1, 1] to range [action_spaces.low, action_spaces.high]. Default to True. :param str action_bound_method: method to bound action to range [-1, 1], can be either "clip" (for simply clipping the action), "tanh" (for applying tanh squashing) for now, or empty string for no bounding. Default to "clip". :param Optional[gym.Space] action_space: env's action space, mandatory if you want to use option "action_scaling" or "action_bound_method". Default to None. :param lr_scheduler: a learning rate scheduler that adjusts the learning rate in optimizer in each policy.update(). Default to None (no lr_scheduler). :param bool deterministic_eval: whether to use deterministic action instead of stochastic action sampled by the policy. Default to False. .. seealso:: Please refer to :class:`~tianshou.policy.BasePolicy` for more detailed explanation. """ def __init__( self, actor: torch.nn.Module, critic: torch.nn.Module, optim: torch.optim.Optimizer, dist_fn: Type[torch.distributions.Distribution], vf_coef: float = 0.5, ent_coef: float = 0.01, max_grad_norm: Optional[float] = None, gae_lambda: float = 0.95, max_batchsize: int = 256, **kwargs: Any ) -> None: super().__init__(actor, optim, dist_fn, **kwargs) self.critic = critic assert 0.0 <= gae_lambda <= 1.0, "GAE lambda should be in [0, 1]." self._lambda = gae_lambda self._weight_vf = vf_coef self._weight_ent = ent_coef self._grad_norm = max_grad_norm self._batch = max_batchsize self._actor_critic = ActorCritic(, self.critic)
[docs] def process_fn( self, batch: Batch, buffer: ReplayBuffer, indices: np.ndarray ) -> Batch: batch = self._compute_returns(batch, buffer, indices) batch.act = to_torch_as(batch.act, batch.v_s) return batch
def _compute_returns( self, batch: Batch, buffer: ReplayBuffer, indices: np.ndarray ) -> Batch: v_s, v_s_ = [], [] with torch.no_grad(): for minibatch in batch.split(self._batch, shuffle=False, merge_last=True): v_s.append(self.critic(minibatch.obs)) v_s_.append(self.critic(minibatch.obs_next)) batch.v_s =, dim=0).flatten() # old value v_s = batch.v_s.cpu().numpy() v_s_ =, dim=0).flatten().cpu().numpy() # when normalizing values, we do not minus self.ret_rms.mean to be numerically # consistent with OPENAI baselines' value normalization pipeline. Emperical # study also shows that "minus mean" will harm performances a tiny little bit # due to unknown reasons (on Mujoco envs, not confident, though). if self._rew_norm: # unnormalize v_s & v_s_ v_s = v_s * np.sqrt(self.ret_rms.var + self._eps) v_s_ = v_s_ * np.sqrt(self.ret_rms.var + self._eps) unnormalized_returns, advantages = self.compute_episodic_return( batch, buffer, indices, v_s_, v_s, gamma=self._gamma, gae_lambda=self._lambda ) if self._rew_norm: batch.returns = unnormalized_returns / \ np.sqrt(self.ret_rms.var + self._eps) self.ret_rms.update(unnormalized_returns) else: batch.returns = unnormalized_returns batch.returns = to_torch_as(batch.returns, batch.v_s) batch.adv = to_torch_as(advantages, batch.v_s) return batch
[docs] def learn( # type: ignore self, batch: Batch, batch_size: int, repeat: int, **kwargs: Any ) -> Dict[str, List[float]]: losses, actor_losses, vf_losses, ent_losses = [], [], [], [] for _ in range(repeat): for minibatch in batch.split(batch_size, merge_last=True): # calculate loss for actor dist = self(minibatch).dist log_prob = dist.log_prob(minibatch.act) log_prob = log_prob.reshape(len(minibatch.adv), -1).transpose(0, 1) actor_loss = -(log_prob * minibatch.adv).mean() # calculate loss for critic value = self.critic(minibatch.obs).flatten() vf_loss = F.mse_loss(minibatch.returns, value) # calculate regularization and overall loss ent_loss = dist.entropy().mean() loss = actor_loss + self._weight_vf * vf_loss \ - self._weight_ent * ent_loss self.optim.zero_grad() loss.backward() if self._grad_norm: # clip large gradient nn.utils.clip_grad_norm_( self._actor_critic.parameters(), max_norm=self._grad_norm ) self.optim.step() actor_losses.append(actor_loss.item()) vf_losses.append(vf_loss.item()) ent_losses.append(ent_loss.item()) losses.append(loss.item()) return { "loss": losses, "loss/actor": actor_losses, "loss/vf": vf_losses, "loss/ent": ent_losses, }