Meta-interpretive learning

Rajan Verma

Abstract


We address the interpretability issue while solving iterative games for games with incomplete information, such as poker. The usage of an opaque feature representation and the use of "black box" fitting techniques like neural networks are the two primary causes of this lack of interpretability. We describe developments on both fronts in this study. In particular, we first suggest a brand-new, condensed, and simple game-state feature representation for Heads-up No-limit (HUNL) Poker. Second, to train our poker bot and create a totally interpretable agent, we combine globally optimal decision trees with a counterfactual regret minimization (CFR) self-play method.

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