Networked Mean Field Games with Elements of Robustness and Learning

This talk will be a fairly high-level one, addressing various current issues in mean field games (MFGs), the underlying challenges primarily with regard to robustness, learning, and incentivization, and paths toward their resolution. Among these are: (i) use of multi-agent reinforcement learning for the computation of mean-field equilibrium (MFE) with state samples drawn from an unmixed Markov chain, and studying the performance of the associated (actor-critic) algorithms; (ii) adversarial MFGs on multi-graphs where agents interact with their neighbors, with such interactions propagating from neighborhoods to the entire network, and with an adversary counteracting the consensus formation process among the agents; and (iii) MFGs with a decision hierarchy, where the agent at the top of the hierarchy (leader) aims at designing incentive strategies (as in mechanism design) to induce a high population of agents at the lower level (followers) to act rationally toward a globally optimal solution in spite of their non-cooperative behavior. The talk will also identify several fruitful directions of research in this domain.