Deep Reinforcement Learning for Wireless Scheduling in Distributed Networked Control

26 Sep 2021  ·  Wanchun Liu, Kang Huang, Daniel E. Quevedo, Branka Vucetic, Yonghui Li ·

We consider a joint uplink and downlink scheduling problem of a fully distributed wireless networked control system (WNCS) with a limited number of frequency channels. Using elements of stochastic systems theory, we derive a sufficient stability condition of the WNCS, which is stated in terms of both the control and communication system parameters. Once the condition is satisfied, there exists a stationary and deterministic scheduling policy that can stabilize all plants of the WNCS. By analyzing and representing the per-step cost function of the WNCS in terms of a finite-length countable vector state, we formulate the optimal transmission scheduling problem into a Markov decision process and develop a deep-reinforcement-learning-based algorithm for solving it. Numerical results show that the proposed algorithm significantly outperforms benchmark policies.

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