強化学習による確率的多エージェントシステムの最適イベントトリガ分散制御
On Optimal Event-Triggered Distributed Control for Stochastic Multi-Agent Systems via Reinforcement Learning
強化学習を用いて、確率的な不確かさを持つ多エージェントシステムの最適分散制御アルゴリズムを提案し、アクター・クリティック・識別器構造とイベントトリガ制御で通信頻度を削減する。
著者: Ziming Wang, Bingbing Li, Karl H. Johansson, Apostolos I. Rikos
分類: eess.SY
原文アブストラクト
We propose a reinforcement learning (RL) based optimal distributed control algorithm for the multi-agent systems (MASs) with stochastic uncertainties. Unlike existing methods, during the optimized backstepping design process, we use the actor-critic-identifier structure. The actor neural network is used to reflect control behavior, the critic neural network works to evaluate control performance and the unknown stochastic uncertainties are handled by identifier neural network. Furthermore, a low-pass filter effectively suppresses problems stemming from non-affine nonlinear faults and a hybrid event-triggered control (ETC) strategy is proposed to reduce control frequency. We analyze our algorithm's operation, and we provide a Lyapunov-based stability proof that guarantees all errors are bounded, ensuring precise tracking between the leader and followers. We validate its correctness in a single-axis robotic manipulator simulation and finally, we compare against the non-optimal control algorithm highlighting our optimal control algorithm's operational advantages.