Progress in the field of Game Theory part2(Reinforcement Learning)

Abstract: Function approximation (FA) has been a critical component in solving large zero-sum games. Yet, little attention has been given towards FA in solving general-sum extensive- form games, despite them being widely regarded as being computationally more challenging than their fully competi- tive or cooperative counterparts. A key challenge is that for many equilibria in general-sum games, no simple analogue to the state value function used in Markov Decision Processes and zero-sum games exists. In this paper, we propose learn- ing the Enforceable Payoff Frontier (EPF) -- a generalization of the state value function for general-sum games. This is the first method that applies FA to the Stackelberg setting, allowing us to scale to much larger games while still enjoying performance guarantees based on FA er- ror.