AI RESEARCH
Neural Co-state Policies: Structuring Hidden States in Recurrent Reinforcement Learning
arXiv CS.LG
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ArXi:2605.05373v1 Announce Type: new A key capability of intelligent agents is operating under partial observability: reasoning and acting effectively despite missing or incomplete state observations. While recurrent (memory-based) policies learned via reinforcement learning address this by encoding history into latent state representations, their internal dynamics remain uninterpretable black boxes. This paper establishes a formal link between these hidden states and the Pontryagin minimum principle (PMP) from optimal control.