600f4bbd55
Preparation for #914 and #920 Changes formatting to ruff and black. Remove python 3.8 ## Additional Changes - Removed flake8 dependencies - Adjusted pre-commit. Now CI and Make use pre-commit, reducing the duplication of linting calls - Removed check-docstyle option (ruff is doing that) - Merged format and lint. In CI the format-lint step fails if any changes are done, so it fulfills the lint functionality. --------- Co-authored-by: Jiayi Weng <jiayi@openai.com>
93 lines
3.6 KiB
Python
93 lines
3.6 KiB
Python
from typing import Any
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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 to_torch
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from tianshou.data.types import RolloutBatchProtocol
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from tianshou.policy import QRDQNPolicy
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class DiscreteCQLPolicy(QRDQNPolicy):
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"""Implementation of discrete Conservative Q-Learning algorithm. arXiv:2006.04779.
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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 num_quantiles: the number of quantile midpoints in the inverse
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cumulative distribution function of the value. Default to 200.
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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 float min_q_weight: the weight for the cql loss.
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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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num_quantiles: int = 200,
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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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min_q_weight: float = 10.0,
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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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num_quantiles,
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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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self._min_q_weight = min_q_weight
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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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all_dist = self(batch).logits
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act = to_torch(batch.act, dtype=torch.long, device=all_dist.device)
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curr_dist = all_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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(dist_diff * (self.tau_hat - (target_dist - curr_dist).detach().le(0.0).float()).abs())
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.sum(-1)
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.mean(1)
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)
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qr_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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# add CQL loss
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q = self.compute_q_value(all_dist, None)
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dataset_expec = q.gather(1, act.unsqueeze(1)).mean()
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negative_sampling = q.logsumexp(1).mean()
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min_q_loss = negative_sampling - dataset_expec
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loss = qr_loss + min_q_loss * self._min_q_weight
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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 {
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"loss": loss.item(),
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"loss/qr": qr_loss.item(),
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"loss/cql": min_q_loss.item(),
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}
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