English(EN) Reward-Estimated Hypergradient for Bilevel Reinforcement Learning with Black-Box Follower

新方法估计双层强化学习中的跟随者奖励

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-09 07:29

研究人员开发了一种名为奖励估计超梯度（RE-HG）的新方法，用于解决跟随者奖励函数未知的双层强化学习场景。RE-HG 使用逆强化学习（IRL）从观察到的轨迹中估计跟随者的奖励。该方法已被证明对线性奖励有效，通过聚合不同动态下的估计来减轻 IRL 的偏差。对于有限样本，RE-HG 通过截断小特征值来稳定估计，实验表明其性能与白盒预言机相当，同时避免了局部最优。 AI

影响在黑盒设置中引入了一种用于双层强化学习的新颖方法，有可能在奖励函数未明确知晓时改善代理的决策制定。

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Hugging Face Daily Papers TIER_1 English(EN) · 2026-11-09 07:29

面向黑盒跟随者的双层强化学习的奖励估计超梯度

Hypergradient (HG) methods for bilevel reinforcement learning (RL) typically assume a white-box follower whose reward is known. In practice, however, the leader rarely knows the follower's reward function and can only observe its trajectories. We address this black-box setting an…

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面向黑盒跟随者的双层强化学习的奖励估计超梯度

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