from typing import Any, Dict, Optional, Union
import numpy as np
import torch
import torch.nn.functional as F
from tianshou.data import Batch, to_numpy
from tianshou.policy import QRDQNPolicy
[docs]class IQNPolicy(QRDQNPolicy):
"""Implementation of Implicit Quantile Network. arXiv:1806.06923.
: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 sample_size: the number of samples for policy evaluation.
Default to 32.
:param int online_sample_size: the number of samples for online model
in training. Default to 8.
:param int target_sample_size: the number of samples for target model
in training. Default to 8.
: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 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.QRDQNPolicy` for more detailed
explanation.
"""
def __init__(
self,
model: torch.nn.Module,
optim: torch.optim.Optimizer,
discount_factor: float = 0.99,
sample_size: int = 32,
online_sample_size: int = 8,
target_sample_size: int = 8,
estimation_step: int = 1,
target_update_freq: int = 0,
reward_normalization: bool = False,
**kwargs: Any,
) -> None:
super().__init__(
model, optim, discount_factor, sample_size, estimation_step,
target_update_freq, reward_normalization, **kwargs
)
assert sample_size > 1, "sample_size should be greater than 1"
assert online_sample_size > 1, "online_sample_size should be greater than 1"
assert target_sample_size > 1, "target_sample_size should be greater than 1"
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: Batch,
state: Optional[Union[dict, Batch, np.ndarray]] = None,
model: str = "model",
input: str = "obs",
**kwargs: Any,
) -> Batch:
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[input]
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 not hasattr(self, "max_action_num"):
self.max_action_num = q.shape[1]
act = to_numpy(q.max(dim=1)[1])
return Batch(logits=logits, act=act, state=hidden, taus=taus)
[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)
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.).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()}