A dynamics and task decoupled reinforcement learning architecture for high-efficiency dynamic target intercept

Dora D. Liu, Liang Hu*, Qi Zhang*, Tangwei Ye, Usman Naseem, Zhong Yuan Lai

*Corresponding author for this work

Research output: Contribution to journalConference paperpeer-review

Abstract

Due to the flexibility and ease of control, unmanned aerial vehicles (UAVs) have been increasingly used in various scenarios and applications in recent years. Training UAVs with reinforcement learning (RL) for a specific task is often expensive in terms of time and computation. However, it is known that the main effort of the learning process is made to fit the low-level physical dynamics systems instead of the high-level task itself. In this paper, we study to apply UAVs in the dynamic target intercept (DTI) task, where the dynamics systems equipped by different UAV models are correspondingly distinct. To this end, we propose a dynamics and task decoupled RL architecture to address the inefficient learning procedure, where the RL module focuses on modeling the DTI task without involving physical dynamics, and the design of states, actions, and rewards are completely task-oriented while the dynamics control module can adaptively convert actions from the RL module to dynamics signals to control different UAVs without retraining the RL module. We show the efficiency and efficacy of our results in comparison and ablation experiments against state-of-the-art methods.

Original languageEnglish
Pages (from-to)12049-12057
Number of pages9
JournalProceedings of the AAAI Conference on Artificial Intelligence
Volume37
Issue number10
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event37th AAAI Conference on Artificial Intelligence, AAAI 2023 - Washington, United States
Duration: 7 Feb 202314 Feb 2023

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