Source code for tianshou.policy.modelfree.a2c

from dataclasses import dataclass
from typing import Any, Generic, Literal, TypeVar, cast

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

from import ReplayBuffer, SequenceSummaryStats, to_torch_as
from import BatchWithAdvantagesProtocol, RolloutBatchProtocol
from tianshou.policy import PGPolicy
from tianshou.policy.base import TLearningRateScheduler, TrainingStats
from import TDistributionFunction
from import ActorCritic

[docs] @dataclass(kw_only=True) class A2CTrainingStats(TrainingStats): loss: SequenceSummaryStats actor_loss: SequenceSummaryStats vf_loss: SequenceSummaryStats ent_loss: SequenceSummaryStats
TA2CTrainingStats = TypeVar("TA2CTrainingStats", bound=A2CTrainingStats) # TODO: the type ignore here is needed b/c the hierarchy is actually broken! Should reconsider the inheritance structure.
[docs] class A2CPolicy(PGPolicy[TA2CTrainingStats], Generic[TA2CTrainingStats]): # type: ignore[type-var] """Implementation of Synchronous Advantage Actor-Critic. arXiv:1602.01783. :param actor: the actor network following the rules in BasePolicy. (s -> logits) :param critic: the critic network. (s -> V(s)) :param optim: the optimizer for actor and critic network. :param dist_fn: distribution class for computing the action. :param action_space: env's action space :param vf_coef: weight for value loss. :param ent_coef: weight for entropy loss. :param max_grad_norm: clipping gradients in back propagation. :param gae_lambda: in [0, 1], param for Generalized Advantage Estimation. :param max_batchsize: the maximum size of the batch when computing GAE. :param discount_factor: in [0, 1]. :param reward_normalization: normalize estimated values to have std close to 1. :param deterministic_eval: if True, use deterministic evaluation. :param observation_space: the space of the observation. :param action_scaling: if True, scale the action from [-1, 1] to the range of action_space. Only used if the action_space is continuous. :param action_bound_method: method to bound action to range [-1, 1]. Only used if the action_space is continuous. :param lr_scheduler: if not None, will be called in `policy.update()`. .. 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: TDistributionFunction, action_space: gym.Space, vf_coef: float = 0.5, ent_coef: float = 0.01, max_grad_norm: float | None = None, gae_lambda: float = 0.95, max_batchsize: int = 256, discount_factor: float = 0.99, # TODO: rename to return_normalization? reward_normalization: bool = False, deterministic_eval: bool = False, observation_space: gym.Space | None = None, action_scaling: bool = True, action_bound_method: Literal["clip", "tanh"] | None = "clip", lr_scheduler: TLearningRateScheduler | None = None, ) -> None: super().__init__( actor=actor, optim=optim, dist_fn=dist_fn, action_space=action_space, discount_factor=discount_factor, reward_normalization=reward_normalization, deterministic_eval=deterministic_eval, observation_space=observation_space, action_scaling=action_scaling, action_bound_method=action_bound_method, lr_scheduler=lr_scheduler, ) self.critic = critic assert 0.0 <= gae_lambda <= 1.0, f"GAE lambda should be in [0, 1] but got: {gae_lambda}" self.gae_lambda = gae_lambda self.vf_coef = vf_coef self.ent_coef = ent_coef self.max_grad_norm = max_grad_norm self.max_batchsize = max_batchsize self._actor_critic = ActorCritic(, self.critic)
[docs] def process_fn( self, batch: RolloutBatchProtocol, buffer: ReplayBuffer, indices: np.ndarray, ) -> BatchWithAdvantagesProtocol: batch = self._compute_returns(batch, buffer, indices) batch.act = to_torch_as(batch.act, batch.v_s) return batch
def _compute_returns( self, batch: RolloutBatchProtocol, buffer: ReplayBuffer, indices: np.ndarray, ) -> BatchWithAdvantagesProtocol: v_s, v_s_ = [], [] with torch.no_grad(): for minibatch in batch.split(self.max_batchsize, 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. Empirical # study also shows that "minus mean" will harm performances a tiny little bit # due to unknown reasons (on Mujoco envs, not confident, though). # TODO: see todo in PGPolicy.process_fn 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.gae_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 cast(BatchWithAdvantagesProtocol, batch) # TODO: mypy complains b/c signature is different from superclass, although # it's compatible. Can this be fixed?
[docs] def learn( # type: ignore self, batch: RolloutBatchProtocol, batch_size: int | None, repeat: int, *args: Any, **kwargs: Any, ) -> TA2CTrainingStats: losses, actor_losses, vf_losses, ent_losses = [], [], [], [] split_batch_size = batch_size or -1 for _ in range(repeat): for minibatch in batch.split(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.vf_coef * vf_loss - self.ent_coef * ent_loss self.optim.zero_grad() loss.backward() if self.max_grad_norm: # clip large gradient nn.utils.clip_grad_norm_( self._actor_critic.parameters(), max_norm=self.max_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()) loss_summary_stat = SequenceSummaryStats.from_sequence(losses) actor_loss_summary_stat = SequenceSummaryStats.from_sequence(actor_losses) vf_loss_summary_stat = SequenceSummaryStats.from_sequence(vf_losses) ent_loss_summary_stat = SequenceSummaryStats.from_sequence(ent_losses) return A2CTrainingStats( # type: ignore[return-value] loss=loss_summary_stat, actor_loss=actor_loss_summary_stat, vf_loss=vf_loss_summary_stat, ent_loss=ent_loss_summary_stat, )