Frontiers of Information Technology & Electronic Engineering
>> 2024,
Volume 25,
Issue 6
doi:
10.1631/FITEE.2300394
Multi-agent reinforcement learning behavioral control for nonlinear second-order systems
Affiliation(s): College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China; 5G+ Industrial Internet Institute, Fuzhou University, Fuzhou 350108, China; less
Received: 2023-06-01
Accepted: 2024-07-05
Available online: 2024-07-05
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Abstract
(RLBC) is limited to an individual agent without any swarm mission, because it models the behavior priority learning as a Markov decision process. In this paper, a novel multi-agent (MARLBC) method is proposed to overcome such limitations by implementing joint learning. Specifically, a multi-agent (MARLMS) is designed for a group of nonlinear to assign the behavior priorities at the decision layer. Through modeling behavior priority switching as a cooperative Markov game, the MARLMS learns an optimal joint behavior priority to reduce dependence on human intelligence and high-performance computing hardware. At the control layer, a group of second-order controllers are designed to learn the optimal control policies to track position and velocity signals simultaneously. In particular, input saturation constraints are strictly implemented via designing a group of adaptive compensators. Numerical simulation results show that the proposed MARLBC has a lower switching frequency and control cost than finite-time and fixed-time and RLBC methods.
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