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Rating strategies in a game is an important area of research in game theoryand artificial intelligence, and can be applied to any real-world competitiveor cooperative setting. Traditionally, only transitive dependencies betweenstrategies have been used to rate strategies (e.g. Elo), however recent workhas expanded ratings to utilize game theoretic solutions to better ratestrategies in non-transitive games. This work generalizes these ideas andproposes novel algorithms suitable for N-player, general-sum rating ofstrategies in normal-form games according to the payoff rating system. Thisenables well-established solution concepts, such as equilibria, to be leveragedto efficiently rate strategies in games with complex strategic interactions,which arise in multiagent training and real-world interactions between manyagents. We empirically validate our methods on real world normal-form data(Premier League) and multiagent reinforcement learning agent evaluation.

Game Theoretic Rating in N-player general-sum games with Equilibria