Q-learning solution for optimal consensus control of discrete-time multiagent systems using reinforcement learning

• Published in: Journal of the Franklin Institute Vol. 356; no. 13; pp. 6946 - 6967
• Main Authors: Mu, Chaoxu, Zhao, Qian, Gao, Zhongke, Sun, Changyin
• Format: Journal Article
• Language: English
• Published: Elmsford Elsevier Ltd 01.09.2019; Elsevier Science Ltd
• Subjects: Algorithms; Computer simulation; Control systems; Discrete element method; Discrete time systems; Exact solutions; Game theory; Iterative methods; Machine learning; Mathematical models; Multiagent systems; Optimal control; Performance indices; Simulation; Stability analysis; Synchronism; System dynamics

This paper investigates a Q-learning scheme for the optimal consensus control of discrete-time multiagent systems. The Q-learning algorithm is conducted by reinforcement learning (RL) using system data instead of system dynamics information. In the multiagent systems, the agents are interacted with...