Source code for tianshou.policy.imitation.discrete_bcq

import math
from typing import Any, Dict, Optional, Union

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

from import Batch, ReplayBuffer, to_torch
from tianshou.policy import DQNPolicy

[docs]class DiscreteBCQPolicy(DQNPolicy): """Implementation of discrete BCQ algorithm. arXiv:1910.01708. :param torch.nn.Module model: a model following the rules in :class:`~tianshou.policy.BasePolicy`. (s -> q_value) :param torch.nn.Module imitator: a model following the rules in :class:`~tianshou.policy.BasePolicy`. (s -> imitation_logits) :param torch.optim.Optimizer optim: a torch.optim for optimizing the model. :param float discount_factor: in [0, 1]. :param int estimation_step: the number of steps to look ahead. Default to 1. :param int target_update_freq: the target network update frequency. :param float eval_eps: the epsilon-greedy noise added in evaluation. :param float unlikely_action_threshold: the threshold (tau) for unlikely actions, as shown in Equ. (17) in the paper. Default to 0.3. :param float imitation_logits_penalty: regularization weight for imitation logits. Default to 1e-2. :param bool reward_normalization: normalize the reward to Normal(0, 1). 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.BasePolicy` for more detailed explanation. """ def __init__( self, model: torch.nn.Module, imitator: torch.nn.Module, optim: torch.optim.Optimizer, 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, **kwargs: Any, ) -> None: super().__init__( model, optim, discount_factor, estimation_step, target_update_freq, reward_normalization, **kwargs ) assert target_update_freq > 0, "BCQ needs target network setting." self.imitator = imitator assert 0.0 <= unlikely_action_threshold < 1.0, \ "unlikely_action_threshold should be in [0, 1)" 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) -> "DiscreteBCQPolicy": = 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} # target_Q = Q_old(s_, argmax(Q_new(s_, *))) act = self(batch, input="obs_next").act target_q, _ = self.model_old(batch.obs_next) target_q = target_q[np.arange(len(act)), act] return target_q
[docs] def forward( # type: ignore self, batch: Batch, state: Optional[Union[dict, Batch, np.ndarray]] = None, input: str = "obs", **kwargs: Any, ) -> Batch: obs = batch[input] q_value, state = self.model(obs, state=state, if not hasattr(self, "max_action_num"): self.max_action_num = q_value.shape[1] imitation_logits, _ = self.imitator(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 - np.inf * mask).argmax(dim=-1) return Batch( act=act, state=state, q_value=q_value, imitation_logits=imitation_logits )
[docs] def learn(self, batch: Batch, **kwargs: Any) -> Dict[str, float]: 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 { "loss": loss.item(), "loss/q": q_loss.item(), "loss/i": i_loss.item(), "loss/reg": reg_loss.item(), }