import math
from typing import Any, Dict, Optional, Union
import numpy as np
import torch
import torch.nn.functional as F
from tianshou.data 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":
self.training = 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, info=batch.info)
if not hasattr(self, "max_action_num"):
self.max_action_num = q_value.shape[1]
imitation_logits, _ = self.imitator(obs, state=state, info=batch.info)
# 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(),
}