from dataclasses import dataclass
from typing import Any, Literal, TypeVar
import gymnasium as gym
import numpy as np
import torch
import torch.nn.functional as F
from tianshou.data import (
ReplayBuffer,
SequenceSummaryStats,
to_numpy,
to_torch,
)
from tianshou.data.types import LogpOldProtocol, RolloutBatchProtocol
from tianshou.policy import PPOPolicy
from tianshou.policy.base import TLearningRateScheduler
from tianshou.policy.modelfree.pg import TDistributionFunction
from tianshou.policy.modelfree.ppo import PPOTrainingStats
[docs]
@dataclass(kw_only=True)
class GailTrainingStats(PPOTrainingStats):
disc_loss: SequenceSummaryStats
acc_pi: SequenceSummaryStats
acc_exp: SequenceSummaryStats
TGailTrainingStats = TypeVar("TGailTrainingStats", bound=GailTrainingStats)
[docs]
class GAILPolicy(PPOPolicy[TGailTrainingStats]):
r"""Implementation of Generative Adversarial Imitation Learning. arXiv:1606.03476.
:param actor: the actor network following the rules in BasePolicy. (s -> logits)
:param critic: the critic network. (s -> V(s))
:param optim: the optimizer for actor and critic network.
:param dist_fn: distribution class for computing the action.
:param action_space: env's action space
:param expert_buffer: the replay buffer containing expert experience.
:param disc_net: the discriminator network with input dim equals
state dim plus action dim and output dim equals 1.
:param disc_optim: the optimizer for the discriminator network.
:param disc_update_num: the number of discriminator grad steps per model grad step.
:param eps_clip: :math:`\epsilon` in :math:`L_{CLIP}` in the original
paper.
:param dual_clip: a parameter c mentioned in arXiv:1912.09729 Equ. 5,
where c > 1 is a constant indicating the lower bound. Set to None
to disable dual-clip PPO.
:param value_clip: a parameter mentioned in arXiv:1811.02553v3 Sec. 4.1.
:param advantage_normalization: whether to do per mini-batch advantage
normalization.
:param recompute_advantage: whether to recompute advantage every update
repeat according to https://arxiv.org/pdf/2006.05990.pdf Sec. 3.5.
:param vf_coef: weight for value loss.
:param ent_coef: weight for entropy loss.
:param max_grad_norm: clipping gradients in back propagation.
:param gae_lambda: in [0, 1], param for Generalized Advantage Estimation.
:param max_batchsize: the maximum size of the batch when computing GAE.
:param discount_factor: in [0, 1].
:param reward_normalization: normalize estimated values to have std close to 1.
:param deterministic_eval: if True, use deterministic evaluation.
:param observation_space: the space of the observation.
:param action_scaling: if True, scale the action from [-1, 1] to the range of
action_space. Only used if the action_space is continuous.
:param action_bound_method: method to bound action to range [-1, 1].
:param lr_scheduler: if not None, will be called in `policy.update()`.
.. 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: TDistributionFunction,
action_space: gym.Space,
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: float | None = None,
value_clip: bool = False,
advantage_normalization: bool = True,
recompute_advantage: bool = False,
vf_coef: float = 0.5,
ent_coef: float = 0.01,
max_grad_norm: float | None = None,
gae_lambda: float = 0.95,
max_batchsize: int = 256,
discount_factor: float = 0.99,
# TODO: rename to return_normalization?
reward_normalization: bool = False,
deterministic_eval: bool = False,
observation_space: gym.Space | None = None,
action_scaling: bool = True,
action_bound_method: Literal["clip", "tanh"] | None = "clip",
lr_scheduler: TLearningRateScheduler | None = None,
) -> None:
super().__init__(
actor=actor,
critic=critic,
optim=optim,
dist_fn=dist_fn,
action_space=action_space,
eps_clip=eps_clip,
dual_clip=dual_clip,
value_clip=value_clip,
advantage_normalization=advantage_normalization,
recompute_advantage=recompute_advantage,
vf_coef=vf_coef,
ent_coef=ent_coef,
max_grad_norm=max_grad_norm,
gae_lambda=gae_lambda,
max_batchsize=max_batchsize,
discount_factor=discount_factor,
reward_normalization=reward_normalization,
deterministic_eval=deterministic_eval,
observation_space=observation_space,
action_scaling=action_scaling,
action_bound_method=action_bound_method,
lr_scheduler=lr_scheduler,
)
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: RolloutBatchProtocol,
buffer: ReplayBuffer,
indices: np.ndarray,
) -> LogpOldProtocol:
"""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: RolloutBatchProtocol) -> 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: RolloutBatchProtocol,
batch_size: int | None,
repeat: int,
**kwargs: Any,
) -> TGailTrainingStats:
# 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
ppo_loss_stat = super().learn(batch, batch_size, repeat, **kwargs)
disc_losses_summary = SequenceSummaryStats.from_sequence(losses)
acc_pi_summary = SequenceSummaryStats.from_sequence(acc_pis)
acc_exps_summary = SequenceSummaryStats.from_sequence(acc_exps)
return GailTrainingStats( # type: ignore[return-value]
**ppo_loss_stat.__dict__,
disc_loss=disc_losses_summary,
acc_pi=acc_pi_summary,
acc_exp=acc_exps_summary,
)
