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

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.

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Electrical Engineering and Systems Science - Systems and Control

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Kesper, L, Trimpe, S & Baumann, D 2023, Toward Multi-Agent Reinforcement Learning for Distributed Event-Triggered Control. in Proceedings of the Learning for Dynamics and Control Conference. vol. 211, Proceedings of Machine Learning Research, JMLR, pp. 1072-1085, Learning for Dynamics and Control Conference, Philadelphia, Pennsylvania, United States, 14/06/2023. < http://10.48550/arXiv.2305.08723 >

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https://urn.fi/URN:NBN:fi:aalto-202306143854

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[article-cris] Sähkötekniikan korkeakoulu / ELEC

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

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