Apostolos I. Rikos

On Optimal Event-Triggered Distributed Control for Stochastic Multi-Agent Systems via Reinforcement Learning

This work proposes a reinforcement learning (RL) based optimal distributed control algorithm for the multi-agent systems (MASs) with stochastic uncertainties that uses the actor-critic-identifier structure and provides a Lyapunov-based stability proof that guarantees all errors are bounded, ensuring precise tracking between the leader and followers.