from typing import Any, Dict, List, Optional, Type
import numpy as np
import torch
import torch.nn.functional as F
from tianshou.data import Batch, ReplayBuffer, to_numpy, to_torch
from tianshou.policy import PPOPolicy
[docs]class GAILPolicy(PPOPolicy):
r"""Implementation of Generative Adversarial Imitation Learning. arXiv:1606.03476.
: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 ReplayBuffer expert_buffer: the replay buffer contains expert experience.
:param torch.nn.Module disc_net: the discriminator network with input dim equals
state dim plus action dim and output dim equals 1.
:param torch.optim.Optimizer disc_optim: the optimizer for the discriminator
network.
:param int disc_update_num: the number of discriminator grad steps per model grad
step. Default to 4.
: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:1811.02553 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.
:param lr_scheduler: a learning rate scheduler that adjusts the learning rate in
optimizer in each policy.update(). Default to None (no lr_scheduler).
.. seealso::
Please refer to :class:`~tianshou.policy.PPOPolicy` 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],
expert_buffer: ReplayBuffer,
disc_net: torch.nn.Module,
disc_optim: torch.optim.Optimizer,
disc_update_num: int = 4,
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, eps_clip, dual_clip, value_clip,
advantage_normalization, recompute_advantage, **kwargs
)
self.disc_net = disc_net
self.disc_optim = disc_optim
self.disc_update_num = disc_update_num
self.expert_buffer = expert_buffer
self.action_dim = actor.output_dim
[docs] def process_fn(
self, batch: Batch, buffer: ReplayBuffer, indices: np.ndarray
) -> Batch:
"""Pre-process the data from the provided replay buffer.
Used in :meth:`update`. Check out :ref:`process_fn` for more information.
"""
# update reward
with torch.no_grad():
batch.rew = to_numpy(-F.logsigmoid(-self.disc(batch)).flatten())
return super().process_fn(batch, buffer, indices)
[docs] def disc(self, batch: Batch) -> torch.Tensor:
obs = to_torch(batch.obs, device=self.disc_net.device)
act = to_torch(batch.act, device=self.disc_net.device)
return self.disc_net(torch.cat([obs, act], dim=1))
[docs] def learn( # type: ignore
self, batch: Batch, batch_size: int, repeat: int, **kwargs: Any
) -> Dict[str, List[float]]:
# update discriminator
losses = []
acc_pis = []
acc_exps = []
bsz = len(batch) // self.disc_update_num
for b in batch.split(bsz, merge_last=True):
logits_pi = self.disc(b)
exp_b = self.expert_buffer.sample(bsz)[0]
logits_exp = self.disc(exp_b)
loss_pi = -F.logsigmoid(-logits_pi).mean()
loss_exp = -F.logsigmoid(logits_exp).mean()
loss_disc = loss_pi + loss_exp
self.disc_optim.zero_grad()
loss_disc.backward()
self.disc_optim.step()
losses.append(loss_disc.item())
acc_pis.append((logits_pi < 0).float().mean().item())
acc_exps.append((logits_exp > 0).float().mean().item())
# update policy
res = super().learn(batch, batch_size, repeat, **kwargs)
res["loss/disc"] = losses
res["stats/acc_pi"] = acc_pis
res["stats/acc_exp"] = acc_exps
return res