利用物理信息多代理反强化学习在分布式无人机群中发现奖励目标

IF 9.4 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Cybernetics Pub Date : 2024-11-13 DOI:10.1109/TCYB.2024.3489967
Adolfo Perrusquía;Weisi Guo
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引用次数: 0

摘要

无人机群的合作性质对服务的顺利运行和国家设施的安全构成了风险。在大多数情况下,由于观察到每个无人机的复杂行为,蜂群的控制目标是闭塞的。理解群体的控制目标是至关重要的,同时更好地理解它们如何相互沟通以实现预期的任务。为了解决这些问题,本文提出了一种物理信息多智能体逆强化学习(PI-MAIRL): 1)从观测数据中推断控制目标函数或奖励函数;2)通过利用每架无人机的物理信息动力学模型来揭示网络拓扑。这些综合的贡献使我们能够更好地理解群体的行为,同时使我们能够对其目标进行推理,进行经验推理和模仿学习。本研究考虑了一个物理不耦合的群体情景。物理信息元素的结合允许获得比无模型IRL算法计算效率更高的算法。在一个全局Riccati方程上用Lyapunov递推验证了该方法的收敛性。仿真研究显示了该方法的优点和挑战。
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Uncovering Reward Goals in Distributed Drone Swarms Using Physics-Informed Multiagent Inverse Reinforcement Learning
The cooperative nature of drone swarms poses risks in the smooth operation of services and the security of national facilities. The control objective of the swarm is, in most cases, occluded due to the complex behaviors observed in each drone. It is paramount to understand which is the control objective of the swarm, whilst understanding better how they communicate with each other to achieve the desired task. To solve these issues, this article proposes a physics-informed multiagent inverse reinforcement learning (PI-MAIRL) that: 1) infers the control objective function or reward function from observational data and 2) uncover the network topology by exploiting a physics-informed model of the dynamics of each drone. The combined contribution enables to understand better the behavior of the swarm, whilst enabling the inference of its objective for experience inference and imitation learning. A physically uncoupled swarm scenario is considered in this study. The incorporation of the physics-informed element allows to obtain an algorithm that is computationally more efficient than model-free IRL algorithms. Convergence of the proposed approach is verified using Lyapunov recursions on a global Riccati equation. Simulation studies are carried out to show the benefits and challenges of the approach.
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来源期刊
IEEE Transactions on Cybernetics
IEEE Transactions on Cybernetics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, CYBERNETICS
CiteScore
25.40
自引率
11.00%
发文量
1869
期刊介绍: The scope of the IEEE Transactions on Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or machine, human, and organizations. The scope includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, cognitive systems, decision making, and robotics, to the extent that they contribute to the theme of cybernetics or demonstrate an application of cybernetics principles.
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