Efficient Control for Cooperation: Communication, Learning and Robustness in Multi-Agent Systems

Besides facing the same challenges as single-agent systems, the distributed nature of complex multi-agent systems sparks many questions and problems revolving around the constraints imposed by communication. The idea that multi-agent systems require communication to access information, to coordinate or simply to sense the environment they are...

Robust Event-Driven Interactions in Cooperative Multi-agent Learning

We present an approach to safely reduce the communication required between agents in a Multi-Agent Reinforcement Learning system by exploiting the inherent robustness of the underlying Markov Decision Process. We compute robustness certificate functions (off-line), that give agents a conservative indication of how far their state measurements...

Event-Based Communication in Distributed Q-Learning

We present an approach to reduce the communication of information needed on a Distributed Q-Learning system inspired by Event Triggered Control (ETC) techniques. We consider a baseline scenario of a Distributed Q-Learning problem on a Markov Decision Process (MDP). Following an event-based approach, N agents sharing a value function explore the...