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News/Discovering What You Can Control: Interventional Boundary Discovery for Reinforcement Learning
arxiv
PublishedMay 8, 2026 at 4:00 AM
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Discovering What You Can Control: Interventional Boundary Discovery for Reinforcement Learning

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arXiv:2603.18257v2 Announce Type: replace-cross Abstract: When an RL agent's observations contain distractors driven by the same confounders as its true state, observational data alone cannot identify which dimensions the agent controls. In our benchmarks, even state-conditioned observational select

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arxivMAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation1harxivCounterfactual Model Testing Under Distribution Shift1harxivCentralized Critics for N-Agent Ad Hoc Teamwork1harxivWhere and What to Reward? Understanding Generalization Between Verifiable and Open-Ended Domains in Reinforcement Learning1h
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