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Multi-agent Reinforcement Learning in OpenSpiel: A Reproduction Report

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arxiv 2103.00187 v2 pith:5EGL5GVB submitted 2021-02-27 cs.AI

Michael Walton , Viliam Lisy This is my paper · ORCID
classification cs.AI
keywords learningopenspielalgorithmsreinforcementreportresultsadditionallycode
verification ladder T0 review T1 audit T2 compute T3 formal
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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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  1. Solving Infinite-Player Games with Player-to-Strategy Networks

    cs.GT 2025-01 conditional novelty 6.0 of 10

    A Player-to-Strategy Network trained with Shared-Parameter Simultaneous Gradient achieves low regret approximate Nash equilibria in five infinite-player games.

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