限られた通信範囲での線形二次ネットワーク制御のための分散方策勾配法
Distributed Policy Gradient for Linear Quadratic Networked Control with Limited Communication Range
各エージェントが近隣とのみ情報交換するマルチエージェント線形二次系において、局所情報のみで勾配を近似するスケーラブルな分散方策勾配法を提案し、通信・制御範囲の拡大に伴い最適解との差が指数的に減少することを示した。
分類: cs.MA, cs.LG, cs.RO, cs.SY, eess.SY
原文アブストラクト
This paper proposes a scalable distributed policy gradient method and proves its convergence to near-optimal solution in multi-agent linear quadratic networked systems. The agents engage within a specified network under local communication constraints, implying that each agent can only exchange information with a limited number of neighboring agents. On the underlying graph of the network, each agent implements its control input depending on its nearby neighbors' states in the linear quadratic control setting. We show that it is possible to approximate the exact gradient only using local information. Compared with the centralized optimal controller, the performance gap decreases to zero exponentially as the communication and control ranges increase. We also demonstrate how increasing the communication range enhances system stability in the gradient descent process, thereby elucidating a critical trade-off. The simulation results verify our theoretical findings.
