Abstract
This paper introduces a deep reinforcement learning method to train an autonomous aerial agent acting as a shepherd to provide guidance for a swarm of ground vehicles. The learner is situated within a high-fidelity robotic-operating-system (ROS)-based simulation environment consisting of an Unmanned Aerial Vehicle (UAV) learning to guide a swarm of Unmanned Ground Vehicles (UGVs) to a target location. Our approach uses a combination of machine education, apprenticeship bootstrapping, and deep-learning-based methodologies to decompose the complex shepherding strategy into sub-problems requiring simpler skills that get fused to form the overall skills required for shepherding. The proposed methodology is effective in training the UAV agent with multiple reward designing schemes.
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Acknowledgement
This material is based upon work supported by the Air Force Office of Scientific Research under award number FA2386-17-1-4054 and an Australian Research Council Discovery Grant DP160102037.
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Nguyen, H.T. et al. (2019). A Deep Hierarchical Reinforcement Learner for Aerial Shepherding of Ground Swarms. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Lecture Notes in Computer Science(), vol 11953. Springer, Cham. https://doi.org/10.1007/978-3-030-36708-4_54
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DOI: https://doi.org/10.1007/978-3-030-36708-4_54
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