import warnings
from dataclasses import dataclass
from typing import Any, Generic, TypeVar
import gymnasium as gym
import numpy as np
import torch
import torch.nn.functional as F
from tianshou.data import Batch, ReplayBuffer
from tianshou.data.types import RolloutBatchProtocol
from tianshou.policy import DQNPolicy
from tianshou.policy.base import TLearningRateScheduler
from tianshou.policy.modelfree.dqn import DQNTrainingStats
[docs]
@dataclass(kw_only=True)
class QRDQNTrainingStats(DQNTrainingStats):
pass
TQRDQNTrainingStats = TypeVar("TQRDQNTrainingStats", bound=QRDQNTrainingStats)
[docs]
class QRDQNPolicy(DQNPolicy[TQRDQNTrainingStats], Generic[TQRDQNTrainingStats]):
"""Implementation of Quantile Regression Deep Q-Network. arXiv:1710.10044.
:param model: a model following the rules in
:class:`~tianshou.policy.BasePolicy`. (s -> logits)
:param optim: a torch.optim for optimizing the model.
:param action_space: Env's action space.
:param discount_factor: in [0, 1].
:param num_quantiles: the number of quantile midpoints in the inverse
cumulative distribution function of the value.
:param estimation_step: the number of steps to look ahead.
:param target_update_freq: the target network update frequency (0 if
you do not use the target network).
:param reward_normalization: normalize the **returns** to Normal(0, 1).
TODO: rename to return_normalization?
:param is_double: use double dqn.
:param clip_loss_grad: clip the gradient of the loss in accordance
with nature14236; this amounts to using the Huber loss instead of
the MSE loss.
:param observation_space: Env's observation space.
:param lr_scheduler: if not None, will be called in `policy.update()`.
.. seealso::
Please refer to :class:`~tianshou.policy.DQNPolicy` for more detailed
explanation.
"""
def __init__(
self,
*,
model: torch.nn.Module,
optim: torch.optim.Optimizer,
action_space: gym.spaces.Discrete,
discount_factor: float = 0.99,
num_quantiles: int = 200,
estimation_step: int = 1,
target_update_freq: int = 0,
reward_normalization: bool = False,
is_double: bool = True,
clip_loss_grad: bool = False,
observation_space: gym.Space | None = None,
lr_scheduler: TLearningRateScheduler | None = None,
) -> None:
assert num_quantiles > 1, f"num_quantiles should be greater than 1 but got: {num_quantiles}"
super().__init__(
model=model,
optim=optim,
action_space=action_space,
discount_factor=discount_factor,
estimation_step=estimation_step,
target_update_freq=target_update_freq,
reward_normalization=reward_normalization,
is_double=is_double,
clip_loss_grad=clip_loss_grad,
observation_space=observation_space,
lr_scheduler=lr_scheduler,
)
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:
obs_next_batch = Batch(
obs=buffer[indices].obs_next,
info=[None] * len(indices),
) # obs_next: s_{t+n}
if self._target:
act = self(obs_next_batch).act
next_dist = self(obs_next_batch, model="model_old").logits
else:
next_batch = self(obs_next_batch)
act = next_batch.act
next_dist = next_batch.logits
return next_dist[np.arange(len(act)), act, :]
[docs]
def compute_q_value(self, logits: torch.Tensor, mask: np.ndarray | None) -> torch.Tensor:
return super().compute_q_value(logits.mean(2), mask)
[docs]
def learn(self, batch: RolloutBatchProtocol, *args: Any, **kwargs: Any) -> TQRDQNTrainingStats:
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(0.0).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 QRDQNTrainingStats(loss=loss.item()) # type: ignore[return-value]
