from typing import Any, Dict, List, Optional, Type
import numpy as np
import torch
from torch import nn
from tianshou.data import Batch, ReplayBuffer, to_torch_as
from tianshou.policy import A2CPolicy
from tianshou.utils.net.common import ActorCritic
[docs]class PPOPolicy(A2CPolicy):
r"""Implementation of Proximal Policy Optimization. arXiv:1707.06347.
: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 eps_clip: :math:`\epsilon` in :math:`L_{CLIP}` in the original
paper. Default to 0.2.
:param float dual_clip: a parameter c mentioned in arXiv:1912.09729 Equ. 5,
where c > 1 is a constant indicating the lower bound.
Default to 5.0 (set None if you do not want to use it).
:param bool value_clip: a parameter mentioned in arXiv:1811.02553v3 Sec. 4.1.
Default to True.
:param bool advantage_normalization: whether to do per mini-batch advantage
normalization. Default to True.
:param bool recompute_advantage: whether to recompute advantage every update
repeat according to https://arxiv.org/pdf/2006.05990.pdf Sec. 3.5.
Default to False.
: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, also normalize the advantage to Normal(0, 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],
eps_clip: float = 0.2,
dual_clip: Optional[float] = None,
value_clip: bool = False,
advantage_normalization: bool = True,
recompute_advantage: bool = False,
**kwargs: Any,
) -> None:
super().__init__(actor, critic, optim, dist_fn, **kwargs)
self._eps_clip = eps_clip
assert dual_clip is None or dual_clip > 1.0, \
"Dual-clip PPO parameter should greater than 1.0."
self._dual_clip = dual_clip
self._value_clip = value_clip
self._norm_adv = advantage_normalization
self._recompute_adv = recompute_advantage
self._actor_critic: ActorCritic
[docs] def process_fn(
self, batch: Batch, buffer: ReplayBuffer, indices: np.ndarray
) -> Batch:
if self._recompute_adv:
# buffer input `buffer` and `indices` to be used in `learn()`.
self._buffer, self._indices = buffer, indices
batch = self._compute_returns(batch, buffer, indices)
batch.act = to_torch_as(batch.act, batch.v_s)
with torch.no_grad():
batch.logp_old = self(batch).dist.log_prob(batch.act)
return batch
[docs] def learn( # type: ignore
self, batch: Batch, batch_size: int, repeat: int, **kwargs: Any
) -> Dict[str, List[float]]:
losses, clip_losses, vf_losses, ent_losses = [], [], [], []
for step in range(repeat):
if self._recompute_adv and step > 0:
batch = self._compute_returns(batch, self._buffer, self._indices)
for minibatch in batch.split(batch_size, merge_last=True):
# calculate loss for actor
dist = self(minibatch).dist
if self._norm_adv:
mean, std = minibatch.adv.mean(), minibatch.adv.std()
minibatch.adv = (minibatch.adv -
mean) / (std + self._eps) # per-batch norm
ratio = (dist.log_prob(minibatch.act) -
minibatch.logp_old).exp().float()
ratio = ratio.reshape(ratio.size(0), -1).transpose(0, 1)
surr1 = ratio * minibatch.adv
surr2 = ratio.clamp(
1.0 - self._eps_clip, 1.0 + self._eps_clip
) * minibatch.adv
if self._dual_clip:
clip1 = torch.min(surr1, surr2)
clip2 = torch.max(clip1, self._dual_clip * minibatch.adv)
clip_loss = -torch.where(minibatch.adv < 0, clip2, clip1).mean()
else:
clip_loss = -torch.min(surr1, surr2).mean()
# calculate loss for critic
value = self.critic(minibatch.obs).flatten()
if self._value_clip:
v_clip = minibatch.v_s + \
(value - minibatch.v_s).clamp(-self._eps_clip, self._eps_clip)
vf1 = (minibatch.returns - value).pow(2)
vf2 = (minibatch.returns - v_clip).pow(2)
vf_loss = torch.max(vf1, vf2).mean()
else:
vf_loss = (minibatch.returns - value).pow(2).mean()
# calculate regularization and overall loss
ent_loss = dist.entropy().mean()
loss = clip_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()
clip_losses.append(clip_loss.item())
vf_losses.append(vf_loss.item())
ent_losses.append(ent_loss.item())
losses.append(loss.item())
return {
"loss": losses,
"loss/clip": clip_losses,
"loss/vf": vf_losses,
"loss/ent": ent_losses,
}