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 Dict, List, Union, Optional

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 torch.distributions.Distribution dist_fn: for computing the action,
        defaults to ``torch.distributions.Categorical``.
    :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.

    .. 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: torch.distributions.Distribution
                 = torch.distributions.Categorical,
                 discount_factor: float = 0.99,
                 vf_coef: float = .5,
                 ent_coef: float = .01,
                 max_grad_norm: Optional[float] = None,
                 gae_lambda: float = 0.95,
                 reward_normalization: bool = False,
                 **kwargs) -> None:
        super().__init__(None, optim, dist_fn, discount_factor, **kwargs)
        self.actor = actor
        self.critic = critic
        assert 0 <= gae_lambda <= 1, 'GAE lambda should be in [0, 1].'
        self._lambda = gae_lambda
        self._w_vf = vf_coef
        self._w_ent = ent_coef
        self._grad_norm = max_grad_norm
        self._batch = 64
        self._rew_norm = reward_normalization

    def process_fn(self, batch: Batch, buffer: ReplayBuffer,
                   indice: np.ndarray) -> Batch:
        if self._lambda in [0, 1]:
            return self.compute_episodic_return(
                batch, None, gamma=self._gamma, gae_lambda=self._lambda)
        v_ = []
        with torch.no_grad():
            for b in batch.split(self._batch, shuffle=False):
                v_.append(to_numpy(self.critic(b.obs_next)))
        v_ = np.concatenate(v_, axis=0)
        return self.compute_episodic_return(
            batch, 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) -> 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)
        act = dist.sample()
        return Batch(logits=logits, act=act, state=h, dist=dist)

    def learn(self, batch: Batch, batch_size: int, repeat: int,
              **kwargs) -> Dict[str, List[float]]:
        self._batch = batch_size
        losses, actor_losses, vf_losses, ent_losses = [], [], [], []
        for _ in range(repeat):
            for b in batch.split(batch_size):
                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(
                    r.shape[0], -1).transpose(0, 1)
                a_loss = -(log_prob * (r - v).detach()).mean()
                vf_loss = F.mse_loss(r, v)
                ent_loss = dist.entropy().mean()
                loss = a_loss + self._w_vf * vf_loss - self._w_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,
        }