Optimal Resource Allocation in Wireless Control Systems via Deep Policy Gradient

TL;DR

This paper develops a deep reinforcement learning approach to optimize resource allocation in wireless control systems, enabling reliable plant control despite noisy channels and limited resources.

Contribution

It introduces a model-free policy gradient method to learn continuous power allocation policies without requiring explicit system models or communication assumptions.

Findings

Learned policies outperform baseline methods in simulations.

Deep RL effectively manages resource allocation under noisy conditions.

Neural network policies adapt to varying channel and plant states.

Abstract

In wireless control systems, remote control of plants is achieved through closing of the control loop over a wireless channel. As wireless communication is noisy and subject to packet dropouts, proper allocation of limited resources, e.g. transmission power, across plants is critical for maintaining reliable operation. In this paper, we formulate the design of an optimal resource allocation policy that uses current plant states and wireless channel states to assign resources used to send control actuation information back to plants. While this problem is challenging due to its infinite dimensionality and need for explicit system model and state knowledge, we propose the use of deep reinforcement learning techniques to find neural network-based resource allocation policies. In particular, we use model-free policy gradient methods to directly learn continuous power allocation policies without knowledge of plant dynamics or communication models. Numerical simulations demonstrate the strong performance of learned policies relative to baseline resource allocation methods in settings where state information is available both with and without noise.