Combining Deep Reinforcement Learning and Search for Imperfect-Information Games
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The combination of deep reinforcement learning and search at both training and test time is a powerful paradigm that has led to a number of a successes in single-agent settings and perfect-information games, best exemplified by the success of AlphaZero. However, algorithms of this form have been unable to cope with imperfect-information games. This paper presents ReBeL, a general framework for self-play reinforcement learning and search for imperfect-information games. In the simpler setting of perfect-information games, ReBeL reduces to an algorithm similar to AlphaZero. Results show ReBeL leads to low exploitability in benchmark imperfect-information games and achieves superhuman performance in heads-up no-limit Texas hold'em poker, while using far less domain knowledge than any prior poker AI. We also prove that ReBeL converges to a Nash equilibrium in two-player zero-sum games in tabular settings.
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