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Apprenticeship bootstrapping: Inverse Reinforcement learning in a multi-skill UAV-UGV coordination task

The Nguyen, H. and Bui, L.T. and Garratt, M. and Abbass, H. (2018) Apprenticeship bootstrapping: Inverse Reinforcement learning in a multi-skill UAV-UGV coordination task. In: 17th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2018, 10 July 2018 through 15 July 2018.

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Abstract

Apprenticeship learning enables learning from human demonstrations performed on tasks. However, acquiring demonstrations in complex tasks where a human expert is not available can be a challenge. In this paper, we propose a new learning algorithm, called Apprenticeship bootstrapping via Inverse Reinforcement Learning using Deep Q-learning (ABS via IRL-DQN), to learn a complex task through using demonstrations performed on primitive sub-tasks. The algorithm is evaluated on an aerial and ground coordination scenario, where an Unmanned Aerial Vehicle (UAV) is required to maintain three Unmanned Ground Vehicles (UGVs) within a field of view of the UAV's camera (FoV). The results show that performance of our proposed algorithm is comparable to that of a human, and competitive to the original IRL using expert demonstrations performed on the composite task. © 2018 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculties > Faculty of Information Technology
Uncontrolled Keywords: Antennas; Apprentices; Autonomous agents; Deep learning; Demonstrations; Ground vehicles; Intelligent vehicle highway systems; Learning algorithms; Multi agent systems; Unmanned aerial vehicles (UAV); Apprenticeship learning; Ground-air interaction; Inverse reinforcement learning; Q-learning; UGVs; Reinforcement learning
Additional Information: Conference code: 139890. Language of original document: English.
URI: http://eprints.lqdtu.edu.vn/id/eprint/9618

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