Tianshou/tianshou/policy/modelfree/a2c.py

import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
from typing import Any, Dict, List, Union, Optional, Callable

from tianshou.policy import PGPolicy
from tianshou.data import Batch, ReplayBuffer, to_torch_as, to_numpy


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: Callable[[], torch.distributions.Distribution]
    :param float discount_factor: in [0, 1], defaults to 0.99.
    :param float vf_coef: weight for value loss, defaults to 0.5.
    :param float ent_coef: weight for entropy loss, defaults to 0.01.
    :param float max_grad_norm: clipping gradients in back propagation,
        defaults to None.
    :param float gae_lambda: in [0, 1], param for Generalized Advantage
        Estimation, defaults to 0.95.
    :param bool reward_normalization: normalize the reward to Normal(0, 1),
        defaults 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;
        defaults to 256.

    .. 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: Callable[[], torch.distributions.Distribution],
        discount_factor: float = 0.99,
        vf_coef: float = 0.5,
        ent_coef: float = 0.01,
        max_grad_norm: Optional[float] = None,
        gae_lambda: float = 0.95,
        reward_normalization: bool = False,
        max_batchsize: int = 256,
        **kwargs: Any
    ) -> None:
        super().__init__(None, optim, dist_fn, discount_factor, **kwargs)
        self.actor = actor
        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._rew_norm = reward_normalization

    def process_fn(
        self, batch: Batch, buffer: ReplayBuffer, indice: np.ndarray
    ) -> Batch:
        if self._lambda in [0.0, 1.0]:
            return self.compute_episodic_return(
                batch, buffer, indice,
                None, gamma=self._gamma, gae_lambda=self._lambda)
        v_ = []
        with torch.no_grad():
            for b in batch.split(self._batch, shuffle=False, merge_last=True):
                v_.append(to_numpy(self.critic(b.obs_next)))
        v_ = np.concatenate(v_, axis=0)
        return self.compute_episodic_return(
            batch, buffer, indice,
            v_, gamma=self._gamma,
            gae_lambda=self._lambda, rew_norm=self._rew_norm)

    def forward(
        self,
        batch: Batch,
        state: Optional[Union[dict, Batch, np.ndarray]] = None,
        **kwargs: Any
    ) -> Batch:
        """Compute action over the given batch data.

        :return: A :class:`~tianshou.data.Batch` which has 4 keys:

            * ``act`` the action.
            * ``logits`` the network's raw output.
            * ``dist`` the action distribution.
            * ``state`` the hidden state.

        .. seealso::

            Please refer to :meth:`~tianshou.policy.BasePolicy.forward` for
            more detailed explanation.
        """
        logits, h = self.actor(batch.obs, state=state, info=batch.info)
        if isinstance(logits, tuple):
            dist = self.dist_fn(*logits)
        else:
            dist = self.dist_fn(logits)  # type: ignore
        act = dist.sample()
        return Batch(logits=logits, act=act, state=h, dist=dist)

    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 b in batch.split(batch_size, merge_last=True):
                self.optim.zero_grad()
                dist = self(b).dist
                v = self.critic(b.obs).flatten()
                a = to_torch_as(b.act, v)
                r = to_torch_as(b.returns, v)
                log_prob = dist.log_prob(a).reshape(len(r), -1).transpose(0, 1)
                a_loss = -(log_prob * (r - v).detach()).mean()
                vf_loss = F.mse_loss(r, v)  # type: ignore
                ent_loss = dist.entropy().mean()
                loss = a_loss + self._weight_vf * vf_loss - \
                    self._weight_ent * ent_loss
                loss.backward()
                if self._grad_norm is not None:
                    nn.utils.clip_grad_norm_(
                        list(self.actor.parameters())
                        + list(self.critic.parameters()),
                        max_norm=self._grad_norm,
                    )
                self.optim.step()
                actor_losses.append(a_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,
        }