Toward Multi-Agent Reinforcement Learning for Distributed Event-Triggered Control

Autor:	Kesper, Lukas, Trimpe, Sebastian, Baumann, Dominik
Rok vydání:	2023
Předmět:	Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
Popis:	Event-triggered communication and control provide high control performance in networked control systems without overloading the communication network. However, most approaches require precise mathematical models of the system dynamics, which may not always be available. Model-free learning of communication and control policies provides an alternative. Nevertheless, existing methods typically consider single-agent settings. This paper proposes a model-free reinforcement learning algorithm that jointly learns resource-aware communication and control policies for distributed multi-agent systems from data. We evaluate the algorithm in a high-dimensional and nonlinear simulation example and discuss promising avenues for further research. Comment: Accepted final version to appear in Proc. of the Conference on Learning for Dynamics and Control, 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2305.08723 Zobrazit plný text záznamu View this record from Arxiv