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Multiagent algorithms often aim to accurately predict the behaviors of otheragents and find a best response accordingly. Previous works usually assume anopponent uses a stationary strategy or randomly switches among severalstationary ones. However, an opponent may exhibit more sophisticated behaviorsby adopting more advanced reasoning strategies, e.g., using a Bayesianreasoning strategy. This paper proposes a novel approach called Bayes-ToMoPwhich can efficiently detect the strategy of opponents using either stationaryor higher-level reasoning strategies. Bayes-ToMoP also supports the detectionof previously unseen policies and learning a best-response policy accordingly.We provide a theoretical guarantee of the optimality on detecting theopponent's strategies. We also propose a deep version of Bayes-ToMoP byextending Bayes-ToMoP with DRL techniques. Experimental results show bothBayes-ToMoP and deep Bayes-ToMoP outperform the state-of-the-art approacheswhen faced with different types of opponents in two-agent competitive games.

Towards Efficient Detection and Optimal Response against Sophisticated Opponents