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...

Multi-Agent Actor-Critic Reinforcement Learning for Cooperative Tasks

For single-agent problems, Reinforcement Learning (RL) algorithms proved to be useful learning optimal control laws for nonlinear dynamic systems without relying on a mathematical model of the system to be controlled. With their ability to work on continuous action and state spaces, actor-critic RL algorithms are especially advantageous in that...