130 lines
4.9 KiB
Python
130 lines
4.9 KiB
Python
import math
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from typing import Any, Dict, Optional, Union
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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_torch
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from tianshou.policy import DQNPolicy
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class DiscreteBCQPolicy(DQNPolicy):
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"""Implementation of discrete BCQ algorithm. arXiv:1910.01708.
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:param torch.nn.Module model: a model following the rules in
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:class:`~tianshou.policy.BasePolicy`. (s -> q_value)
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:param torch.nn.Module imitator: a model following the rules in
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:class:`~tianshou.policy.BasePolicy`. (s -> imitation_logits)
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:param torch.optim.Optimizer optim: a torch.optim for optimizing the model.
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:param float discount_factor: in [0, 1].
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:param int estimation_step: the number of steps to look ahead. Default to 1.
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:param int target_update_freq: the target network update frequency.
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:param float eval_eps: the epsilon-greedy noise added in evaluation.
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:param float unlikely_action_threshold: the threshold (tau) for unlikely
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actions, as shown in Equ. (17) in the paper. Default to 0.3.
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:param float imitation_logits_penalty: regularization weight for imitation
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logits. Default to 1e-2.
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:param bool reward_normalization: normalize the reward to Normal(0, 1).
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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.BasePolicy` 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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model: torch.nn.Module,
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imitator: torch.nn.Module,
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optim: torch.optim.Optimizer,
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discount_factor: float = 0.99,
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estimation_step: int = 1,
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target_update_freq: int = 8000,
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eval_eps: float = 1e-3,
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unlikely_action_threshold: float = 0.3,
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imitation_logits_penalty: float = 1e-2,
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reward_normalization: bool = False,
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**kwargs: Any,
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) -> None:
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super().__init__(
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model, optim, discount_factor, estimation_step, target_update_freq,
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reward_normalization, **kwargs
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)
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assert target_update_freq > 0, "BCQ needs target network setting."
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self.imitator = imitator
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assert 0.0 <= unlikely_action_threshold < 1.0, \
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"unlikely_action_threshold should be in [0, 1)"
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if unlikely_action_threshold > 0:
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self._log_tau = math.log(unlikely_action_threshold)
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else:
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self._log_tau = -np.inf
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assert 0.0 <= eval_eps < 1.0
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self.eps = eval_eps
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self._weight_reg = imitation_logits_penalty
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def train(self, mode: bool = True) -> "DiscreteBCQPolicy":
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self.training = mode
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self.model.train(mode)
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self.imitator.train(mode)
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return self
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def _target_q(self, buffer: ReplayBuffer, indices: np.ndarray) -> torch.Tensor:
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batch = buffer[indices] # batch.obs_next: s_{t+n}
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# target_Q = Q_old(s_, argmax(Q_new(s_, *)))
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act = self(batch, input="obs_next").act
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target_q, _ = self.model_old(batch.obs_next)
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target_q = target_q[np.arange(len(act)), act]
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return target_q
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def forward( # type: ignore
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self,
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batch: Batch,
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state: Optional[Union[dict, Batch, np.ndarray]] = None,
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input: str = "obs",
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**kwargs: Any,
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) -> Batch:
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obs = batch[input]
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q_value, state = self.model(obs, state=state, info=batch.info)
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if not hasattr(self, "max_action_num"):
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self.max_action_num = q_value.shape[1]
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imitation_logits, _ = self.imitator(obs, state=state, info=batch.info)
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# mask actions for argmax
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ratio = imitation_logits - imitation_logits.max(dim=-1, keepdim=True).values
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mask = (ratio < self._log_tau).float()
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act = (q_value - np.inf * mask).argmax(dim=-1)
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return Batch(
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act=act, state=state, q_value=q_value, imitation_logits=imitation_logits
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)
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def learn(self, batch: Batch, **kwargs: Any) -> Dict[str, float]:
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if self._iter % self._freq == 0:
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self.sync_weight()
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self._iter += 1
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target_q = batch.returns.flatten()
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result = self(batch)
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imitation_logits = result.imitation_logits
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current_q = result.q_value[np.arange(len(target_q)), batch.act]
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act = to_torch(batch.act, dtype=torch.long, device=target_q.device)
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q_loss = F.smooth_l1_loss(current_q, target_q)
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i_loss = F.nll_loss(F.log_softmax(imitation_logits, dim=-1), act)
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reg_loss = imitation_logits.pow(2).mean()
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loss = q_loss + i_loss + self._weight_reg * reg_loss
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self.optim.zero_grad()
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loss.backward()
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self.optim.step()
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return {
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"loss": loss.item(),
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"loss/q": q_loss.item(),
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"loss/i": i_loss.item(),
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"loss/reg": reg_loss.item(),
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}
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