142 lines
6.5 KiB
Python
142 lines
6.5 KiB
Python
from typing import Any, Dict, List, Optional, Type
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import numpy as np
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import torch
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import torch.nn.functional as F
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from tianshou.data import Batch, ReplayBuffer, to_numpy, to_torch
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from tianshou.policy import PPOPolicy
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class GAILPolicy(PPOPolicy):
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r"""Implementation of Generative Adversarial Imitation Learning. arXiv:1606.03476.
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:param torch.nn.Module actor: the actor network following the rules in
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:class:`~tianshou.policy.BasePolicy`. (s -> logits)
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:param torch.nn.Module critic: the critic network. (s -> V(s))
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:param torch.optim.Optimizer optim: the optimizer for actor and critic network.
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:param dist_fn: distribution class for computing the action.
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:type dist_fn: Type[torch.distributions.Distribution]
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:param ReplayBuffer expert_buffer: the replay buffer contains expert experience.
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:param torch.nn.Module disc_net: the discriminator network with input dim equals
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state dim plus action dim and output dim equals 1.
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:param torch.optim.Optimizer disc_optim: the optimizer for the discriminator
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network.
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:param int disc_update_num: the number of discriminator grad steps per model grad
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step. Default to 4.
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:param float discount_factor: in [0, 1]. Default to 0.99.
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:param float eps_clip: :math:`\epsilon` in :math:`L_{CLIP}` in the original
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paper. Default to 0.2.
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:param float dual_clip: a parameter c mentioned in arXiv:1912.09729 Equ. 5,
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where c > 1 is a constant indicating the lower bound.
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Default to 5.0 (set None if you do not want to use it).
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:param bool value_clip: a parameter mentioned in arXiv:1811.02553 Sec. 4.1.
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Default to True.
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:param bool advantage_normalization: whether to do per mini-batch advantage
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normalization. Default to True.
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:param bool recompute_advantage: whether to recompute advantage every update
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repeat according to https://arxiv.org/pdf/2006.05990.pdf Sec. 3.5.
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Default to False.
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:param float vf_coef: weight for value loss. Default to 0.5.
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:param float ent_coef: weight for entropy loss. Default to 0.01.
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:param float max_grad_norm: clipping gradients in back propagation. Default to
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None.
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:param float gae_lambda: in [0, 1], param for Generalized Advantage Estimation.
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Default to 0.95.
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:param bool reward_normalization: normalize estimated values to have std close
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to 1, also normalize the advantage to Normal(0, 1). Default to False.
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:param int max_batchsize: the maximum size of the batch when computing GAE,
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depends on the size of available memory and the memory cost of the model;
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should be as large as possible within the memory constraint. Default to 256.
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:param bool action_scaling: whether to map actions from range [-1, 1] to range
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[action_spaces.low, action_spaces.high]. Default to True.
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:param str action_bound_method: method to bound action to range [-1, 1], can be
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either "clip" (for simply clipping the action), "tanh" (for applying tanh
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squashing) for now, or empty string for no bounding. Default to "clip".
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:param Optional[gym.Space] action_space: env's action space, mandatory if you want
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to use option "action_scaling" or "action_bound_method". Default to None.
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:param lr_scheduler: a learning rate scheduler that adjusts the learning rate in
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optimizer in each policy.update(). Default to None (no lr_scheduler).
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:param bool deterministic_eval: whether to use deterministic action instead of
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stochastic action sampled by the policy. Default to False.
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:param lr_scheduler: a learning rate scheduler that adjusts the learning rate in
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optimizer in each policy.update(). Default to None (no lr_scheduler).
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.. seealso::
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Please refer to :class:`~tianshou.policy.PPOPolicy` for more detailed
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explanation.
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"""
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def __init__(
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self,
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actor: torch.nn.Module,
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critic: torch.nn.Module,
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optim: torch.optim.Optimizer,
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dist_fn: Type[torch.distributions.Distribution],
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expert_buffer: ReplayBuffer,
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disc_net: torch.nn.Module,
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disc_optim: torch.optim.Optimizer,
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disc_update_num: int = 4,
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eps_clip: float = 0.2,
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dual_clip: Optional[float] = None,
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value_clip: bool = False,
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advantage_normalization: bool = True,
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recompute_advantage: bool = False,
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**kwargs: Any,
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) -> None:
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super().__init__(
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actor, critic, optim, dist_fn, eps_clip, dual_clip, value_clip,
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advantage_normalization, recompute_advantage, **kwargs
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)
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self.disc_net = disc_net
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self.disc_optim = disc_optim
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self.disc_update_num = disc_update_num
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self.expert_buffer = expert_buffer
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self.action_dim = actor.output_dim
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def process_fn(
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self, batch: Batch, buffer: ReplayBuffer, indices: np.ndarray
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) -> Batch:
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"""Pre-process the data from the provided replay buffer.
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Used in :meth:`update`. Check out :ref:`process_fn` for more information.
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"""
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# update reward
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with torch.no_grad():
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batch.rew = to_numpy(-F.logsigmoid(-self.disc(batch)).flatten())
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return super().process_fn(batch, buffer, indices)
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def disc(self, batch: Batch) -> torch.Tensor:
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obs = to_torch(batch.obs, device=self.disc_net.device)
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act = to_torch(batch.act, device=self.disc_net.device)
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return self.disc_net(torch.cat([obs, act], dim=1))
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def learn( # type: ignore
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self, batch: Batch, batch_size: int, repeat: int, **kwargs: Any
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) -> Dict[str, List[float]]:
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# update discriminator
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losses = []
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acc_pis = []
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acc_exps = []
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bsz = len(batch) // self.disc_update_num
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for b in batch.split(bsz, merge_last=True):
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logits_pi = self.disc(b)
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exp_b = self.expert_buffer.sample(bsz)[0]
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logits_exp = self.disc(exp_b)
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loss_pi = -F.logsigmoid(-logits_pi).mean()
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loss_exp = -F.logsigmoid(logits_exp).mean()
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loss_disc = loss_pi + loss_exp
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self.disc_optim.zero_grad()
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loss_disc.backward()
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self.disc_optim.step()
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losses.append(loss_disc.item())
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acc_pis.append((logits_pi < 0).float().mean().item())
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acc_exps.append((logits_exp > 0).float().mean().item())
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# update policy
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res = super().learn(batch, batch_size, repeat, **kwargs)
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res["loss/disc"] = losses
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res["stats/acc_pi"] = acc_pis
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res["stats/acc_exp"] = acc_exps
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return res
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