arXiv AI October 7, 2026 By Abhijeet Krishnan, Colin M. Potts, Arnav Jhala, Harshad Khadilkar, Shirish Karande, Chris Martens

Learning Explainable Representations of Complex Game-playing Strategies

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The paper proposes a method that mimics human cognition to train reinforcement learning agents to generate explainable strategies and policies as executable procedures derived from sequences of gameplay actions. It demonstrates automatic learning of such programs for chess and a grid‑based environment, showing that the resulting strategies produce effective actions and can be learned from gameplay data.

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