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

Sample-Efficient Hypergradient Estimation for Decentralized Bi-Level Reinforcement Learning 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. In many situations, a fundamental challenge arises