Optimize Mobile Wireless Power Transfer by Finite State Machine Reinforcement Learning

This paper addresses the optimization problems in far-field wireless power transfer systems using Reinforcement Learning techniques. The mobile Radio-Frequency(RF) wireless transmitter can charge the nearby harvested energy enabled Internet of Things(IoT) devices. The wireless transmitter intends to find the optimal path to charge all stationary IoT devices in the shortest time. The Reinforcement Learning is applied to determine both the order of the IoT devices to be charged and the shortest path for the robot to drive from one IoT device to another. Due to the high complexity of the task and the charging environment, the traditional Reinforcement Learning even cannot converge. In order to solve this problem, a Finite State Machine Reinforcement Learning is proposed. At first, a heuristic algorithm is utilized to calculate the optimal path between any two IoT devices. Next, using the idea of the Finite State Machine in designing sequential circuit, the proposed algorithm determines the order of the IoT devices to be charged by setting each IoT device as a particular state. As a result, both the order of the IoT devices and the path from one device to another can be figured out. The numerical results prove the superiority of the Finite State Machine Reinforcement Learning in solving the proposed optimization problems. The real wireless power transfer system is also accomplished in our experimental field.