A deep hierarchical reinforcement learner for aerial shepherding of ground swarms
Date
2019
Authors
Nguyen, H.T.
Nguyen, T.D.
Garratt, M.
Kasmarik, K.
Anavatti, S.
Barlow, M.
Abbass, H.A.
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Book chapter
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Event/exhibition information: 26th International Conference on Neural Information Processing, ICONIP 2019, Sydney, Australia, 12/12/2019-15/12/2019
Source details - Title: International Conference on Neural Information ProcessingI CONIP 2019: Neural Information Processing, 2019, pp.658-669
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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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Copyright 2019 Springer Nature