Reinforcement Learning for Multiple HAPS/UAV Coordination: Impact of Exploration-Exploitation Dilemma on Convergence.

Anicho, Ogbonnaya and Charlesworth, Philip B and Baicher, Gurvinder S and Nagar, Atulya (2019) Reinforcement Learning for Multiple HAPS/UAV Coordination: Impact of Exploration-Exploitation Dilemma on Convergence. In: 9th International Conference on Soft Computing for Problem Solving - SocProS 2019, SEPTEMBER 02-04, 2019, LIVERPOOL HOPE UNIVERSITY, LIVERPOOL, UK.

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Abstract

This work analyses the application of Reinforcement Learning (RL) for coordinating multiple Unmanned High Altitude Platform Stations (HAPS) or Unmanned Aerial Vehicles (UAVs) for providing communications area coverage to a community of fixed and mobile users. Multiple agent coordination techniques are essential for developing autonomous capabilities for multi-UAV/HAPS control and management. This paper examines the impact of exploration-exploitation dilemma on the application of RL for coordinating multiple UAVs/HAPS. In the work, it is observed that RL convergence is a challenge, as the RL algorithm struggles to find optimal positioning for maximum user coverage. This paper attempts to establish the source of the convergence issue with the RL technique for this specific application scenario. The work goes on to suggest methods to minimise this impact, and some insights for applying RL techniques for multi-agent coordination for communications area coverage.

Item Type: Conference or Workshop Item (Paper)
Additional Information and Comments: This is the author's version of a paper that was accepted, and presented, at the 9th International Conference on Soft Computing for Problem Solving - SocProS 2019 at Liverpool Hope University.
Faculty / Department: Faculty of Science > Mathematics and Computer Science
Depositing User: Ogbonnaya Anicho
Date Deposited: 28 Oct 2019 14:20
Last Modified: 28 Oct 2019 14:20
URI: http://hira.hope.ac.uk/id/eprint/2962

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