Synergies between evolutionary computation and multiagent reinforcement learning: the benefits of exchanging solutions

Ana L. C. Bazzan

Abstract: In many real-world situations in which resources are scarce, aligning the optimum of the system with the optimum of agents can be conflicting. For instance, in traffic assignment, the system's and the agents' welfare may not be aligned. In order to deal with this, in this paper a new approach is proposed, based on a synergy between: (i) a global optimization process in which the traffic authority employs metaheuristics, and (ii) reinforcement learning processes that run at each individual driver agent. Both the agents and the system authority exchange solutions that are incorporated by the other party in order to come up with an assignment of routes.