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Model-Based Reinforcement Learning in Differential Graphical Games
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DOI：10.1109/TCNS.2016.2617622.png)
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摘要
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This paper seeks to combine differential game theory with the actor-critic-identifier architecture to determine forward-in-time, approximate optimal controllers for formation tracking in multiagent systems, where the agents have uncertain heterogeneous nonlinear dynamics. A continuous control strategy is proposed, using communication feedback from extended neighbors on a communication topology that has a spanning tree. A model-based reinforcement learning technique is developed to cooperatively control a group of agents to track a trajectory in a desired formation. Simulation results are presented to demonstrate the performance of the developed technique.
Keyword:
Dynamic programming
graphical games
learning systems
networked control systems
optimal control
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5
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被引数:
5.8K
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