Multiagent Evaluation under Incomplete Information

NeurIPS 2019 Mark RowlandShayegan OmidshafieiKarl TuylsJulien PerolatMichal ValkoGeorgios PiliourasRemi Munos

This paper investigates the evaluation of learned multiagent strategies in the incomplete information setting, which plays a critical role in ranking and training of agents. Traditionally, researchers have relied on Elo ratings for this purpose, with recent works also using methods based on Nash equilibria... (read more)

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