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 tianshou.data import Batch, ReplayBuffer, to_torch_as
from tianshou.policy import PGPolicy
from tianshou.utils.net.common 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.actor, 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 = 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. 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())
# update learning rate if lr_scheduler is given
if self.lr_scheduler is not None:
self.lr_scheduler.step()
return {
"loss": losses,
"loss/actor": actor_losses,
"loss/vf": vf_losses,
"loss/ent": ent_losses,
}