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Games have a long history as benchmarks for progress in artificialintelligence. Approaches using search and learning produced strong performanceacross many perfect information games, and approaches using game-theoreticreasoning and learning demonstrated strong performance for specific imperfectinformation poker variants. We introduce Student of Games, a general-purposealgorithm that unifies previous approaches, combining guided search, self-playlearning, and game-theoretic reasoning. Student of Games achieves strongempirical performance in large perfect and imperfect information games -- animportant step towards truly general algorithms for arbitrary environments. Weprove that Student of Games is sound, converging to perfect play as availablecomputation and approximation capacity increases. Student of Games reachesstrong performance in chess and Go, beats the strongest openly available agentin heads-up no-limit Texas hold'em poker, and defeats the state-of-the-artagent in Scotland Yard, an imperfect information game that illustrates thevalue of guided search, learning, and game-theoretic reasoning.

Student of Games: A unified learning algorithm for both perfect and imperfect information games