import warnings
from typing import Any, Dict, Optional
import numpy as np
import torch
import torch.nn.functional as F
from tianshou.data import Batch, ReplayBuffer
from tianshou.policy import DQNPolicy
[docs]class QRDQNPolicy(DQNPolicy):
"""Implementation of Quantile Regression Deep Q-Network. arXiv:1710.10044.
: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.
.. seealso::
Please refer to :class:`~tianshou.policy.DQNPolicy` 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,
**kwargs: Any,
) -> None:
super().__init__(
model, optim, discount_factor, estimation_step, target_update_freq,
reward_normalization, **kwargs
)
assert num_quantiles > 1, "num_quantiles should be greater than 1"
self._num_quantiles = num_quantiles
tau = torch.linspace(0, 1, self._num_quantiles + 1)
self.tau_hat = torch.nn.Parameter(
((tau[:-1] + tau[1:]) / 2).view(1, -1, 1), requires_grad=False
)
warnings.filterwarnings("ignore", message="Using a target size")
def _target_q(self, buffer: ReplayBuffer, indices: np.ndarray) -> torch.Tensor:
batch = buffer[indices] # batch.obs_next: s_{t+n}
if self._target:
act = self(batch, input="obs_next").act
next_dist = self(batch, model="model_old", input="obs_next").logits
else:
next_batch = self(batch, input="obs_next")
act = next_batch.act
next_dist = next_batch.logits
next_dist = next_dist[np.arange(len(act)), act, :]
return next_dist # shape: [bsz, num_quantiles]
[docs] def compute_q_value(
self, logits: torch.Tensor, mask: Optional[np.ndarray]
) -> torch.Tensor:
return super().compute_q_value(logits.mean(2), mask)
[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)
curr_dist = self(batch).logits
act = batch.act
curr_dist = curr_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)
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
loss.backward()
self.optim.step()
self._iter += 1
return {"loss": loss.item()}