arXiv AI
Learning to Play: Reinforcement Learning and Games
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The Surprising Effectiveness of Approximate Value Iteration in Self-Play
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Towards the Automatic Synthesis of Interpretable Chess Tactics
The paper presents a symbolic sub‑policy model for chess that incorporates domain knowledge to improve interpretability. By adapting patterns learned through inductive logic programming (PAL), the authors evaluate the model using a divergence metric against a random baseline and show that it can suggest moves comparable to a human beginner. They also propose a computational evaluation scheme that augments an existing engine with their model.