Model-free algorithm for consensus of discrete-time multi-agent systems using reinforcement learning method |
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| Institution: | 1. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Wuhan 430074, China;2. Artificial Intelligence School, Wuchang University of Technology, Wuhan 430223, China;1. School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China;2. Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313000, China;3. Navigation college, Dalian Maritime University, Dalian 116026, China |
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| Abstract: | In this work, we investigate consensus issues of discrete-time (DT) multi-agent systems (MASs) with completely unknown dynamic by using reinforcement learning (RL) technique. Different from policy iteration (PI) based algorithms that require admissible initial control policies, this work proposes a value iteration (VI) based model-free algorithm for consensus of DTMASs with optimal performance and no requirement of admissible initial control policy. Firstly, in order to utilize RL method, the consensus problem is modeled as an optimal control problem of tracking error system for each agent. Then, we introduce a VI algorithm for consensus of DTMASs and give a novel convergence analysis for this algorithm, which does not require admissible initial control input. To implement the proposed VI algorithm to achieve consensus of DTMASs without information of dynamics, we construct actor-critic networks to online estimate the value functions and optimal control inputs in real time. At last, we give some simulation results to show the validity of the proposed algorithm. |
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