2cc34fb72b
Closes #914 Additional changes: - Deprecate python below 11 - Remove 3rd party and throughput tests. This simplifies install and test pipeline - Remove gym compatibility and shimmy - Format with 3.11 conventions. In particular, add `zip(..., strict=True/False)` where possible Since the additional tests and gym were complicating the CI pipeline (flaky and dist-dependent), it didn't make sense to work on fixing the current tests in this PR to then just delete them in the next one. So this PR changes the build and removes these tests at the same time.
128 lines
4.9 KiB
Python
128 lines
4.9 KiB
Python
from typing import Any, cast
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import numpy as np
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import torch
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import torch.nn.functional as F
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from tianshou.data import Batch, to_numpy
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from tianshou.data.batch import BatchProtocol
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from tianshou.data.types import QuantileRegressionBatchProtocol, RolloutBatchProtocol
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from tianshou.policy import QRDQNPolicy
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class IQNPolicy(QRDQNPolicy):
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"""Implementation of Implicit Quantile Network. arXiv:1806.06923.
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:param torch.nn.Module model: a model following the rules in
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:class:`~tianshou.policy.BasePolicy`. (s -> logits)
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:param torch.optim.Optimizer optim: a torch.optim for optimizing the model.
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:param float discount_factor: in [0, 1].
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:param int sample_size: the number of samples for policy evaluation.
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Default to 32.
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:param int online_sample_size: the number of samples for online model
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in training. Default to 8.
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:param int target_sample_size: the number of samples for target model
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in training. Default to 8.
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:param int estimation_step: the number of steps to look ahead. Default to 1.
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:param int target_update_freq: the target network update frequency (0 if
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you do not use the target network).
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:param bool reward_normalization: normalize the reward to Normal(0, 1).
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Default to False.
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:param lr_scheduler: a learning rate scheduler that adjusts the learning rate in
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optimizer in each policy.update(). Default to None (no lr_scheduler).
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.. seealso::
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Please refer to :class:`~tianshou.policy.QRDQNPolicy` for more detailed
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explanation.
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"""
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def __init__(
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self,
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model: torch.nn.Module,
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optim: torch.optim.Optimizer,
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discount_factor: float = 0.99,
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sample_size: int = 32,
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online_sample_size: int = 8,
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target_sample_size: int = 8,
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estimation_step: int = 1,
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target_update_freq: int = 0,
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reward_normalization: bool = False,
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**kwargs: Any,
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) -> None:
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super().__init__(
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model,
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optim,
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discount_factor,
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sample_size,
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estimation_step,
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target_update_freq,
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reward_normalization,
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**kwargs,
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)
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assert sample_size > 1, "sample_size should be greater than 1"
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assert online_sample_size > 1, "online_sample_size should be greater than 1"
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assert target_sample_size > 1, "target_sample_size should be greater than 1"
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self._sample_size = sample_size # for policy eval
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self._online_sample_size = online_sample_size
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self._target_sample_size = target_sample_size
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def forward(
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self,
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batch: RolloutBatchProtocol,
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state: dict | BatchProtocol | np.ndarray | None = None,
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model: str = "model",
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input: str = "obs",
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**kwargs: Any,
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) -> QuantileRegressionBatchProtocol:
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if model == "model_old":
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sample_size = self._target_sample_size
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elif self.training:
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sample_size = self._online_sample_size
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else:
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sample_size = self._sample_size
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model = getattr(self, model)
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obs = batch[input]
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obs_next = obs.obs if hasattr(obs, "obs") else obs
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(logits, taus), hidden = model(
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obs_next,
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sample_size=sample_size,
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state=state,
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info=batch.info,
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)
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q = self.compute_q_value(logits, getattr(obs, "mask", None))
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if not hasattr(self, "max_action_num"):
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self.max_action_num = q.shape[1]
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act = to_numpy(q.max(dim=1)[1])
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result = Batch(logits=logits, act=act, state=hidden, taus=taus)
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return cast(QuantileRegressionBatchProtocol, result)
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def learn(self, batch: RolloutBatchProtocol, *args: Any, **kwargs: Any) -> dict[str, float]:
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if self._target and self._iter % self._freq == 0:
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self.sync_weight()
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self.optim.zero_grad()
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weight = batch.pop("weight", 1.0)
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action_batch = self(batch)
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curr_dist, taus = action_batch.logits, action_batch.taus
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act = batch.act
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curr_dist = curr_dist[np.arange(len(act)), act, :].unsqueeze(2)
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target_dist = batch.returns.unsqueeze(1)
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# calculate each element's difference between curr_dist and target_dist
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dist_diff = F.smooth_l1_loss(target_dist, curr_dist, reduction="none")
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huber_loss = (
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(
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dist_diff
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* (taus.unsqueeze(2) - (target_dist - curr_dist).detach().le(0.0).float()).abs()
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)
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.sum(-1)
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.mean(1)
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)
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loss = (huber_loss * weight).mean()
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# ref: https://github.com/ku2482/fqf-iqn-qrdqn.pytorch/
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# blob/master/fqf_iqn_qrdqn/agent/qrdqn_agent.py L130
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batch.weight = dist_diff.detach().abs().sum(-1).mean(1) # prio-buffer
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loss.backward()
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self.optim.step()
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self._iter += 1
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return {"loss": loss.item()}
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