IEEE Member-only icon Solving Zero-sum Games through Reinforcement Learning - IEEE CoG2022 Tutorial III Solving Zero-sum Games through Reinforcement Learning - IEEE CoG2022 Tutorial III

Solving Zero-sum Games through Reinforcement Learning - IEEE CoG2022 Tutorial III

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Tutorial III of 2022 IEEE Conference on Games given by Yaodong Yang and Le Cong Dinh, with the title of Solving Zero-sum Games through Reinforcement Learning.

Recent advances in multiagent reinforcement learning have introduced a new learning paradigm around population-based training. The idea is to consider the structure of games not at the micro-level of individual actions but at the meta-level of which agent to train against for any given game or situation.

A typical framework of population-based training is the Policy Space Response Oracle (PSRO) method, where, at each iteration, a new Reinforcement Learning agent is discovered as the best response to a Nash mixture of agents from the opponent populations. PSRO methods can provably converge to Nash, correlated, and coarse correlated equilibria in N-player games; particularly, they have shown remarkable performance in solving large-scale zero-sum games.

In this tutorial, the speaker introduces the basic idea of PSRO methods, the necessity of using PSRO methods in solving real-world games such as Chess, the recent results on solving N-player games and mean-field games, how to promote behavioral diversity during training, and the relationship of PSRO method to the conventional no-regret methods.

Finally, a new meta-PSRO framework named Neural Auto-Curricula is introduced, where we make AI learning to learn a PSRO-like solution algorithm purely from data, and a new PSRO framework called online double oracle that inherits the benefits from both population-based methods and no-regret methods.

Tutorial III of 2022 IEEE Conference on Games given by Yaodong Yang and Le Cong Dinh, with the title of Solving Zero-sum Games through Reinforcement Learning.

Recent advances in multiagent reinforcement learning have introduced a new learning paradigm around...

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