arXiv cs.AI· Yanjun Chen, Jinghan Wang, Xiaoyu Shen, Wenjie Li, Wei Zhang·· 10 天前AI 评分22
动作塑形:策略只吸收其能表达的东西
Action Shaping: Policies Absorb What They Can Express
AI 导读
论文提出"动作塑形"概念:训练时在动作通道加入偏移、部署时移除,这一做法此前没有理论保证。作者给出吸收条件——可训练策略只能吸收其输出层能精确复现的偏移,最小实例是学习门控后的零初始化线性头,在 20 个任务上移除该头几乎无代价。精确复现使损失出现平坦方向,偏移幅度可在移除前预示代价。
正文 · 原文
Abstract:Reward shaping has a theorem: a potential-based term can be removed without changing the optimal policy. The same practice on the action channel, an offset added in training and dropped at deployment, has no theorem. Nothing cancels an action offset, so the correction is kept at deployment or removed without a guarantee. We call it action shaping and state its principle. A trainable policy absorbs an offset its own output layer can reproduce exactly, which is what we mean by express; what is absorbed can be removed with the return intact. Its minimal instance is a zero-initialized linear head behind a learnable gate, added to an actor that trains through a learned action-value function, with no penalty or schedule. The gate rises and then falls on its own, for deterministic and stochastic actors alike, and on 20 tasks removing the head costs almost nothing. The condition is exact reproduction, not capacity: a nonlinear head with more parameters is not absorbed, and in a paired control, one linear path added to a nonlinear base head restores absorption. Exact reproduction gives the loss a flat direction that gradient noise drifts along, and the offset's amplitude indicates, before removal, what dropping the head will cost. Action shaping thus gains the counterpart of the shaping theorem, a condition for absorption, together with the mechanism behind it and a diagnostic that reads it. Policies absorb what they can express, and only that.
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.32752 [cs.AI] |
| (or arXiv:2609.32752v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2609.32752 arXiv-issued DOI via DataCite |
Submission history
From: Yanjun Chen [view email]
[v1]
Sat, 26 Sep 2026 16:18:55 UTC (781 KB)
[v2]
Tue, 6 Oct 2026 15:32:07 UTC (777 KB)
来源:arXiv cs.AI · arxiv.org