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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/FITEE.2300394


Multi-agent reinforcement learning behavioral control for nonlinear second-order systems


Author(s):  Zhenyi ZHANG, Jie HUANG, Congjie PAN

Affiliation(s):  College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China; more

Corresponding email(s):   jie.huang@fzu.edu.cn

Key Words:  Reinforcement learning, Behavioral control, Second-order systems, Mission supervisor


Zhenyi ZHANG, Jie HUANG, Congjie PAN. Multi-agent reinforcement learning behavioral control for nonlinear second-order systems[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

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Abstract: 
reinforcement learning behavioral control (RLBC) is limited to individual agent without any swarm mission, because it models the behavior priority learning as a Markov decision process. In this research, a novel multi-agent reinforcement learning behavioral control (MARLBC) is proposed to overcome such limitations by implementing joint learning. Specifically, a multi-agent reinforcement learning mission supervisor (MARLMS) is designed for a group of nonlinear second-order systems to assign the behavior priorities at 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 reinforcement learning controllers (SORLC) is 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 behavioral control and RLBC methods.

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