A Player-to-Strategy Network trained with Shared-Parameter Simultaneous Gradient achieves low regret approximate Nash equilibria in five infinite-player games.
Multi-agent Reinforcement Learning in OpenSpiel: A Reproduction Report
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abstract
In this report, we present results reproductions for several core algorithms implemented in the OpenSpiel framework for learning in games. The primary contribution of this work is a validation of OpenSpiel's re-implemented search and Reinforcement Learning algorithms against the results reported in their respective originating works. Additionally, we provide complete documentation of hyperparameters and source code required to reproduce these experiments easily and exactly.
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Solving Infinite-Player Games with Player-to-Strategy Networks
A Player-to-Strategy Network trained with Shared-Parameter Simultaneous Gradient achieves low regret approximate Nash equilibria in five infinite-player games.