Tactical UAV path planning under radar threat using reinforcement learning involves particular challenges ranging from modeling related difficulties to sparse feedback problem. Learning goal-directed behavior with sparse feedback from complex environments is a fundamental challenge for reinforcement learning algorithms. In this paper we extend our previous work in this area to provide a solution to the problem setting stated above, using Hierarchical Reinforcement Learning (HRL) in a novel way that involves a meta controller for higher level goal assignment and a controller that determines the lower-level actions of the agent. Our meta controller is based on a regression model trained using a state transition scheme that defines the evolution of goal designation, whereas our lower-level controller is based on a Deep Q Network (DQN) and is trained via reinforcement learning iterations. This two-layer framework ensures that an optimal plan for a complex path, organized as multiple goals, is achieved gradually, through piecewise assignment of sub-goals, and thus as a result of a staged, efficient and rigorous procedure.
hierarchical reinforcement learning tactical UAV path planning machine learning path optimization
Birincil Dil | İngilizce |
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Konular | Mühendislik |
Bölüm | TJST |
Yazarlar | |
Yayımlanma Tarihi | 29 Mart 2023 |
Gönderilme Tarihi | 16 Aralık 2022 |
Yayımlandığı Sayı | Yıl 2023 Cilt: 18 Sayı: 1 |