A Proofs Lemma 1. For the mixed imaged opponent policy (IOP) π

According to Bayes' theorem, as we update the posterior probability as The changing trends of α are diverse when against different opponents. IOP to accurately model the opponent policy. Figure 7: Performance against different types of opponents, i.e., fixed policy, naïve learner, and Figure 8: Performance against different types of opponents, i.e., fixed policy, naïve learner, and Note that M = 1 is MBOM w/o IOPs. Figure 9: Performance against different types of opponents, i.e., fixed policy, naïve learner, and reasoning learner in Predator-Prey, where x -axis is joint opponent index. Figure 9 shows the performance when against different types of opponents compared with the baselines. For each type, there are ten test joint opponent policies.