from dataclasses import dataclass
from typing import Any, Literal, TypeVar, cast
import gymnasium as gym
import numpy as np
import torch
import torch.nn.functional as F
from tianshou.data import Batch, to_numpy
from tianshou.data.batch import BatchProtocol
from tianshou.data.types import (
ObsBatchProtocol,
QuantileRegressionBatchProtocol,
RolloutBatchProtocol,
)
from tianshou.policy import QRDQNPolicy
from tianshou.policy.base import TLearningRateScheduler
from tianshou.policy.modelfree.qrdqn import QRDQNTrainingStats
[docs]
@dataclass(kw_only=True)
class IQNTrainingStats(QRDQNTrainingStats):
pass
TIQNTrainingStats = TypeVar("TIQNTrainingStats", bound=IQNTrainingStats)
[docs]
class IQNPolicy(QRDQNPolicy[TIQNTrainingStats]):
"""Implementation of Implicit Quantile Network. arXiv:1806.06923.
:param model: a model following the rules in
:class:`~tianshou.policy.BasePolicy`. (s -> logits)
:param optim: a torch.optim for optimizing the model.
:param discount_factor: in [0, 1].
:param sample_size: the number of samples for policy evaluation.
:param online_sample_size: the number of samples for online model
in training.
:param target_sample_size: the number of samples for target model
in training.
: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()`.
Please refer to :class:`~tianshou.policy.QRDQNPolicy` 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,
sample_size: int = 32,
online_sample_size: int = 8,
target_sample_size: int = 8,
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 sample_size > 1, f"sample_size should be greater than 1 but got: {sample_size}"
assert (
online_sample_size > 1
), f"online_sample_size should be greater than 1 but got: {online_sample_size}"
assert (
target_sample_size > 1
), f"target_sample_size should be greater than 1 but got: {target_sample_size}"
super().__init__(
model=model,
optim=optim,
action_space=action_space,
discount_factor=discount_factor,
num_quantiles=num_quantiles,
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.sample_size = sample_size # for policy eval
self.online_sample_size = online_sample_size
self.target_sample_size = target_sample_size
[docs]
def forward(
self,
batch: ObsBatchProtocol,
state: dict | BatchProtocol | np.ndarray | None = None,
model: Literal["model", "model_old"] = "model",
**kwargs: Any,
) -> QuantileRegressionBatchProtocol:
if model == "model_old":
sample_size = self.target_sample_size
elif self.training:
sample_size = self.online_sample_size
else:
sample_size = self.sample_size
model = getattr(self, model)
obs = batch.obs
# TODO: this seems very contrived!
obs_next = obs.obs if hasattr(obs, "obs") else obs
(logits, taus), hidden = model(
obs_next,
sample_size=sample_size,
state=state,
info=batch.info,
)
q = self.compute_q_value(logits, getattr(obs, "mask", None))
if self.max_action_num is None: # type: ignore
# TODO: see same thing in DQNPolicy!
self.max_action_num = q.shape[1]
act = to_numpy(q.max(dim=1)[1])
result = Batch(logits=logits, act=act, state=hidden, taus=taus)
return cast(QuantileRegressionBatchProtocol, result)
[docs]
def learn(self, batch: RolloutBatchProtocol, *args: Any, **kwargs: Any) -> TIQNTrainingStats:
if self._target and self._iter % self.freq == 0:
self.sync_weight()
self.optim.zero_grad()
weight = batch.pop("weight", 1.0)
action_batch = self(batch)
curr_dist, taus = action_batch.logits, action_batch.taus
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
* (taus.unsqueeze(2) - (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 IQNTrainingStats(loss=loss.item()) # type: ignore[return-value]
