Learning Event-triggered Algorithms for Networked Control Systems
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URL
Journal Title
Journal ISSN
Volume Title
Sähkötekniikan korkeakoulu |
Master's thesis
Authors
Date
2024-08-19
Department
Major/Subject
Control, Robotics and Autonomous Systems
Mcode
ELEC3025
Degree programme
AEE - Master’s Programme in Automation and Electrical Engineering (TS2013)
Language
en
Pages
54+2
Series
Abstract
Incorporating reinforcement learning into event-triggered communication and control policies can achieve impressive control performance and considerable communication savings. Recent approaches rely on centralized training and decentralized execution paradigm to offset the missing global information, which may not always be accessible in the real world. Meanwhile, existing methods for multi-agent systems still face scalability and classical communication problems. To address these issues, we introduce a decentralized reinforcement learning algorithm with selective parameter sharing to learn hybrid action spaces in distributed multi-agent systems. Furthermore, we propose a dynamic penalty design algorithm to balance the control and communication policies.Description
Supervisor
Baumann , DominikThesis advisor
Baumann , DominikKeywords
eent-triggered control, multi-agent system, reinforcement learning, reward shaping