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Large-Scale Multiagent System Tracking Control Using Mean Field Games
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DOI：10.1109/TNNLS.2021.3071109.png)
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Abstract
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This article studies the tracking control problem with a large-scale group of agents. Unlike traditional control techniques used in multiagent systems (MASs), a new type of intelligent design is needed to handle the intractable ``Curse of Dimensionality caused by the extremely large number of agents. To address this challenge, the mean field game (MFG) theory has been embedded into reinforcement learning to advance intelligent tracking control with large-scale MAS. Specifically, MFG-based control can calculate the optimal strategy based on one unified fix-dimension probability density function (pdf) instead of high-dimensional large-scale MAS information collected from individual agents. Moreover, the approximate dynamic programming technique is adopted to generate a new type of MFG-based algorithm. Each agent has three neural networks (NNs) to approximate the solution of the mean field type control. In addition to the algorithm development, the performance of the NNs is also analyzed using the Lyapunov method. Finally, the linear and nonlinear tracking control simulations are given to evaluate the algorithm's performance.
Keywords:
Optimal control
Mathematical model
Artificial neural networks
Games
Probability density function
Trajectory
Cost function
Mean field game (MFG)
optimal control
reinforcement learning
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