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- W4210560253 abstract "We propose an online adaptive synchronization method for leader-follower networks of heterogeneous agents. Distributed Model Reference Adaptive Control (DMRAC-RL) achieves synchronization that enables the improved performance of Reinforcement Learning (RL)-trained policies on a reference model. The leader observes the performance of the reference model, and the followers observe the states and actions of the agents they are connected to, but not the reference model. Notably, both the leader and followers models might differ from the reference model the RL-control policy was trained. DMRAC-RL uses an internal loop that adjusts the learned policy for the agents in the form of an augmented input to solve the distributed control problem. We show that the DMRACRL controller synchronizes the network of heterogeneous agents asymptotically. Numerical examples of the synchronization of a network of inverted pendulums support our theoretical findings." @default.
- W4210560253 created "2022-02-08" @default.
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- W4210560253 date "2021-12-14" @default.
- W4210560253 modified "2023-10-18" @default.
- W4210560253 title "Model Reference Adaptive Control for Online Policy Adaptation and Network Synchronization" @default.
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- W4210560253 doi "https://doi.org/10.1109/cdc45484.2021.9683267" @default.
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