Just add Pepper: extending learning algorithms for repeated matrix games to repeated Markov games

Learning in multi-agent settings has recently garnered much interest, the result of which has been the development of somewhat effective multi-agent learning (MAL) algorithms for repeated normal-form games. However, general-purpose MAL algorithms for richer environments, such as general-sum repeated stochastic (Markov) games (RSGs), are less advanced. Indeed, previously created MAL algorithms for RSGs are typically successful only when the behavior of associates meets specific game theoretic assumptions and when the game is of a particular class (such as zero-sum games). In this paper, we present a new algorithm, called Pepper, that can be used to extend MAL algorithms designed for repeated normal-form games to RSGs. We demonstrate that Pepper creates a family of new algorithms, each of whose asymptotic performance in RSGs is reminiscent of its asymptotic performance in related repeated normal-form games. We also show that some algorithms formed with Pepper outperform existing algorithms in an interesting RSG.