from typing import Any, Dict
import numpy as np
import torch
import torch.nn.functional as F
from tianshou.data import Batch, to_torch
from tianshou.policy import QRDQNPolicy
[docs]class DiscreteCQLPolicy(QRDQNPolicy):
"""Implementation of discrete Conservative Q-Learning algorithm. arXiv:2006.04779.
:param torch.nn.Module model: a model following the rules in
:class:`~tianshou.policy.BasePolicy`. (s -> logits)
:param torch.optim.Optimizer optim: a torch.optim for optimizing the model.
:param float discount_factor: in [0, 1].
:param int num_quantiles: the number of quantile midpoints in the inverse
cumulative distribution function of the value. Default to 200.
:param int estimation_step: the number of steps to look ahead. Default to 1.
:param int target_update_freq: the target network update frequency (0 if
you do not use the target network).
:param bool reward_normalization: normalize the reward to Normal(0, 1).
Default to False.
:param float min_q_weight: the weight for the cql loss.
.. seealso::
Please refer to :class:`~tianshou.policy.QRDQNPolicy` for more detailed
explanation.
"""
def __init__(
self,
model: torch.nn.Module,
optim: torch.optim.Optimizer,
discount_factor: float = 0.99,
num_quantiles: int = 200,
estimation_step: int = 1,
target_update_freq: int = 0,
reward_normalization: bool = False,
min_q_weight: float = 10.0,
**kwargs: Any,
) -> None:
super().__init__(
model, optim, discount_factor, num_quantiles, estimation_step,
target_update_freq, reward_normalization, **kwargs
)
self._min_q_weight = min_q_weight
[docs] def learn(self, batch: Batch, **kwargs: Any) -> Dict[str, float]:
if self._target and self._iter % self._freq == 0:
self.sync_weight()
self.optim.zero_grad()
weight = batch.pop("weight", 1.0)
all_dist = self(batch).logits
act = to_torch(batch.act, dtype=torch.long, device=all_dist.device)
curr_dist = all_dist[np.arange(len(act)), act, :].unsqueeze(2)
target_dist = batch.returns.unsqueeze(1)
# calculate each element's difference between curr_dist and target_dist
dist_diff = F.smooth_l1_loss(target_dist, curr_dist, reduction="none")
huber_loss = (
dist_diff *
(self.tau_hat - (target_dist - curr_dist).detach().le(0.).float()).abs()
).sum(-1).mean(1)
qr_loss = (huber_loss * weight).mean()
# ref: https://github.com/ku2482/fqf-iqn-qrdqn.pytorch/
# blob/master/fqf_iqn_qrdqn/agent/qrdqn_agent.py L130
batch.weight = dist_diff.detach().abs().sum(-1).mean(1) # prio-buffer
# add CQL loss
q = self.compute_q_value(all_dist, None)
dataset_expec = q.gather(1, act.unsqueeze(1)).mean()
negative_sampling = q.logsumexp(1).mean()
min_q_loss = negative_sampling - dataset_expec
loss = qr_loss + min_q_loss * self._min_q_weight
loss.backward()
self.optim.step()
self._iter += 1
return {
"loss": loss.item(),
"loss/qr": qr_loss.item(),
"loss/cql": min_q_loss.item(),
}