Cooperative Multi-Agent Systems from the Reinforcement Learning Perspective -- Challenges, Algorithms, and an Application

Reinforcement Learning has established as a framework that allows an autonomous agent for automatically acquiring -- in a trial and error-based manner -- a behavior policy based on a specification of the desired behavior of the system. In a multi-agent system, however, the decentralization of the control and observation of the system among independent agents has a significant impact on learning and it complexity. In this survey talk, we briefly review the foundations of single-agent reinforcement learning, point to the merits and challenges when applied in a multi-agent setting, and illustrate its potential in the context of an application from the field of manufacturing control and scheduling