Tianshou/tianshou/policy/modelfree/a2c.py

from collections.abc import Callable
from typing import Any, Literal, cast

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

from tianshou.data import ReplayBuffer, to_torch_as
from tianshou.data.types import BatchWithAdvantagesProtocol, RolloutBatchProtocol
from tianshou.policy import PGPolicy
from tianshou.policy.base import TLearningRateScheduler
from tianshou.policy.modelfree.pg import TDistributionFunction
from tianshou.utils.net.common import ActorCritic


class A2CPolicy(PGPolicy):
    """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.actor, self.critic)

    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 = torch.cat(v_s, dim=0).flatten()  # old value
        v_s = batch.v_s.cpu().numpy()
        v_s_ = torch.cat(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?
    def learn(  # type: ignore
        self,
        batch: RolloutBatchProtocol,
        batch_size: int,
        repeat: int,
        *args: Any,
        **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.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())

        return {
            "loss": losses,
            "loss/actor": actor_losses,
            "loss/vf": vf_losses,
            "loss/ent": ent_losses,
        }