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arXiv AI By Mikoto Kudo, Takumi Tanabe, Akifumi Wachi, Youhei Akimoto

Sample-Efficient Hypergradient Estimation for Decentralized Bi-Level Reinforcement Learning

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arXiv:2603. 14867v4 Announce Type: replace-cross Abstract: Many strategic decision-making problems, such as environment design for warehouse robots, can be naturally formulated as bi-level reinforcement learning (RL), where a leader agent optimizes its objective while a follower solves a Markov decision process (MDP) conditioned on the leader's decisions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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