Energy management for object tracking under the energy-harvesting: Hierarchical reinforcement learning method.

This paper explores the target localization problem with signal transmitters which are powered by energy harvesting (EH) devices. Due to the remote transmission and random energy harvesting, the energy supplied to transmitters to transmit information is often insufficient, resulting in packet dropout. The rate of packet dropouts is influenced mainly by the distance from the target to the transmitter and transmission energy. Therefore, this paper aims to investigate the energy allocation policies to ensure the desired positioning performance using a two-level hierarchical framework. Initially, a lower-level policy works on a specified time scale and aims at minimize error covariances with prescribed distance to meet the step objective. Subsequently, a higher-level policy plans the step time scale and calls for the desired step objectives for the lower-level policy. Both policy levels are trained through deep reinforcement learning. Finally, an example is presented to illustrate the efficiency of the designed strategy. • A optimal energy allocation issue is studied for the object tracking under the energy-harvesting transmitters. • The distance information and Signal-to-Noise Rate are taken into account to design the energy allocation. • The hierarchical reinforcement learning is used to design the two behaviors of energy collection and energy utilization. • An optimal energy allocation strategy is obtained in a unified way in terms of the Bellman equation. [ABSTRACT FROM AUTHOR]