Source code for tianshou.policy.imitation.discrete_bcq

import math
from dataclasses import dataclass
from typing import Any, Self, TypeVar, cast

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

from import Batch, ReplayBuffer, to_torch
from import (
from tianshou.policy import DQNPolicy
from tianshou.policy.base import TLearningRateScheduler
from tianshou.policy.modelfree.dqn import DQNTrainingStats

float_info = torch.finfo(torch.float32)
INF = float_info.max

[docs] @dataclass(kw_only=True) class DiscreteBCQTrainingStats(DQNTrainingStats): q_loss: float i_loss: float reg_loss: float
TDiscreteBCQTrainingStats = TypeVar("TDiscreteBCQTrainingStats", bound=DiscreteBCQTrainingStats)
[docs] class DiscreteBCQPolicy(DQNPolicy[TDiscreteBCQTrainingStats]): """Implementation of discrete BCQ algorithm. arXiv:1910.01708. :param model: a model following the rules in :class:`~tianshou.policy.BasePolicy`. (s -> q_value) :param imitator: a model following the rules in :class:`~tianshou.policy.BasePolicy`. (s -> imitation_logits) :param optim: a torch.optim for optimizing the model. :param discount_factor: in [0, 1]. :param estimation_step: the number of steps to look ahead :param target_update_freq: the target network update frequency. :param eval_eps: the epsilon-greedy noise added in evaluation. :param unlikely_action_threshold: the threshold (tau) for unlikely actions, as shown in Equ. (17) in the paper. :param imitation_logits_penalty: regularization weight for imitation logits. :param estimation_step: the number of steps to look ahead. :param target_update_freq: the target network update frequency (0 if you do not use the target network). :param reward_normalization: normalize the **returns** to Normal(0, 1). TODO: rename to return_normalization? :param is_double: use double dqn. :param clip_loss_grad: clip the gradient of the loss in accordance with nature14236; this amounts to using the Huber loss instead of the MSE loss. :param observation_space: Env's observation space. :param lr_scheduler: if not None, will be called in `policy.update()`. .. seealso:: Please refer to :class:`~tianshou.policy.BasePolicy` for more detailed explanation. """ def __init__( self, *, model: torch.nn.Module, imitator: torch.nn.Module, optim: torch.optim.Optimizer, action_space: gym.spaces.Discrete, discount_factor: float = 0.99, estimation_step: int = 1, target_update_freq: int = 8000, eval_eps: float = 1e-3, unlikely_action_threshold: float = 0.3, imitation_logits_penalty: float = 1e-2, reward_normalization: bool = False, is_double: bool = True, clip_loss_grad: bool = False, observation_space: gym.Space | None = None, lr_scheduler: TLearningRateScheduler | None = None, ) -> None: super().__init__( model=model, optim=optim, action_space=action_space, discount_factor=discount_factor, estimation_step=estimation_step, target_update_freq=target_update_freq, reward_normalization=reward_normalization, is_double=is_double, clip_loss_grad=clip_loss_grad, observation_space=observation_space, lr_scheduler=lr_scheduler, ) assert ( target_update_freq > 0 ), f"BCQ needs target_update_freq>0 but got: {target_update_freq}." self.imitator = imitator assert ( 0.0 <= unlikely_action_threshold < 1.0 ), f"unlikely_action_threshold should be in [0, 1) but got: {unlikely_action_threshold}" if unlikely_action_threshold > 0: self._log_tau = math.log(unlikely_action_threshold) else: self._log_tau = -np.inf assert 0.0 <= eval_eps < 1.0 self.eps = eval_eps self._weight_reg = imitation_logits_penalty
[docs] def train(self, mode: bool = True) -> Self: = mode self.model.train(mode) self.imitator.train(mode) return self
def _target_q(self, buffer: ReplayBuffer, indices: np.ndarray) -> torch.Tensor: batch = buffer[indices] # batch.obs_next: s_{t+n} next_obs_batch = Batch(obs=batch.obs_next, info=[None] * len(batch)) # target_Q = Q_old(s_, argmax(Q_new(s_, *))) act = self(next_obs_batch).act target_q, _ = self.model_old(batch.obs_next) return target_q[np.arange(len(act)), act]
[docs] def forward( # type: ignore self, batch: ObsBatchProtocol, state: dict | Batch | np.ndarray | None = None, **kwargs: Any, ) -> ImitationBatchProtocol: # TODO: Liskov substitution principle is violated here, the superclass # produces a batch with the field logits, but this one doesn't. # Should be fixed in the future! q_value, state = self.model(batch.obs, state=state, if self.max_action_num is None: self.max_action_num = q_value.shape[1] imitation_logits, _ = self.imitator(batch.obs, state=state, # mask actions for argmax ratio = imitation_logits - imitation_logits.max(dim=-1, keepdim=True).values mask = (ratio < self._log_tau).float() act = (q_value - INF * mask).argmax(dim=-1) result = Batch(act=act, state=state, q_value=q_value, imitation_logits=imitation_logits) return cast(ImitationBatchProtocol, result)
[docs] def learn( self, batch: RolloutBatchProtocol, *args: Any, **kwargs: Any, ) -> TDiscreteBCQTrainingStats: if self._iter % self.freq == 0: self.sync_weight() self._iter += 1 target_q = batch.returns.flatten() result = self(batch) imitation_logits = result.imitation_logits current_q = result.q_value[np.arange(len(target_q)), batch.act] act = to_torch(batch.act, dtype=torch.long, device=target_q.device) q_loss = F.smooth_l1_loss(current_q, target_q) i_loss = F.nll_loss(F.log_softmax(imitation_logits, dim=-1), act) reg_loss = imitation_logits.pow(2).mean() loss = q_loss + i_loss + self._weight_reg * reg_loss self.optim.zero_grad() loss.backward() self.optim.step() return DiscreteBCQTrainingStats( # type: ignore[return-value] loss=loss.item(), q_loss=q_loss.item(), i_loss=i_loss.item(), reg_loss=reg_loss.item(), )