Source code for tianshou.policy.imitation.gail

from typing import Any, Dict, List, Optional, Type

import numpy as np
import torch
import torch.nn.functional as F

from 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 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([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