Distributed multi-agent target search and tracking with Gaussian process
  and reinforcement learning
Jigang Kim Dohyun Jang H. Jin Kim
Deploying multiple robots for target search and tracking has many practical
applications, yet the challenge of planning over unknown or partially known
targets remains difficult to address. With recent advances in deep learning,
intelligent control techniques such as reinforcement learning have enabled
agents to learn autonomously from environment interactions with little to no
prior knowledge. Such methods can address the exploration-exploitation tradeoff
of planning over unknown targets in a data-driven manner, eliminating the
reliance on heuristics typical of traditional approaches and streamlining the
decision-making pipeline with end-to-end training. In this paper, we propose a
multi-agent reinforcement learning technique with target map building based on
distributed Gaussian process. We leverage the distributed Gaussian process to
encode belief over the target locations and efficiently plan over unknown
targets. We evaluate the performance and transferability of the trained policy
in simulation and demonstrate the method on a swarm of micro unmanned aerial
vehicles with hardware experiments.