In this paper, departing from the existing literature on IL for MFGs, we introduce a new solution concept called the Nash imitation gap.

Most reinforcement learning (RL) algorithms hinge on the Markovian assumption, i.e. that the underlying system transitions and rewards are Markovian in some natural notion of (observable)

Recent advances in constrained reinforcement learning (RL) have endowed reinforcement learning with certain safety guarantees.