Targets search and detection encompasses a variety of decision problems such as coverage, surveillance, search, observing and pursuit-evasion along with others. In this paper we develop a multi-agent deep reinforcement learning (MADRL) method to coordinate a group of aerial vehicles (drones) for the purpose of locating a set of static targets in an unknown area. To that end, we have designed a realistic drone simulator that replicates the dynamics and perturbations of a real experiment, including statistical inferences taken from experimental data for its modeling. Our reinforcement learning method, which utilized this simulator for training, was able to find near-optimal policies for the drones. In contrast to other state-of-the-art MADRL methods, our method is fully decentralized during both learning and execution, can handle high-dimensional and continuous observation spaces, and does not require tuning of additional hyperparameters.
@article{arxiv.2103.09520,
title = {Decentralized Reinforcement Learning for Multi-Target Search and Detection by a Team of Drones},
author = {Roi Yehoshua and Juan Heredia-Juesas and Yushu Wu and Christopher Amato and Jose Martinez-Lorenzo},
journal= {arXiv preprint arXiv:2103.09520},
year = {2021}
}