基于无人机系统群的协同搜救强化学习

Ross D. Arnold, M. Osinski, Christopher Reddy, Austin Lowey
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引用次数: 1

摘要

这项工作通过将深度强化学习(DRL)的有效性与协作多智能体系统中的包容架构的能力相结合,提出了一种新的搜索和救援方法,以及其他广域搜索问题。我们提出了一种多智能体参数共享的深度强化学习范式,其动作空间由一系列预制“角色”的激活组成。每个角色都是在包容启发的层次结构中组织的个人行为的集合。我们描述了我们方法的低成本实现及其使用基本DRL算法、DQN和简单奖励信号的结果。仅使用少量的训练时间和力量,我们就能够看到群性能优于基线统计方法的改进。这些结果表明,我们的技术可以扩展到在各种各样的问题集上获得更好的性能结果。此外,该技术可以扩展到群体和协作多智能体系统空间中的许多问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reinforcement Learning for Collaborative Search and Rescue Using Unmanned Aircraft System Swarms
This work presents a novel approach to search and rescue, and other wide-area search problems, by combining the effectiveness of deep reinforcement learning (DRL) with the power of Subsumption architecture in a collaborative multi-agent system. We present a multi-agent parameter-sharing deep reinforcement learning paradigm with an action space consisting of the activation of an array of premade “roles.” Each role is a collection of individual behaviors organized in a Subsumption-inspired hierarchy. We describe a low-cost implementation of our approach and its results using a basic DRL algorithm, DQN, and a simple reward signal. Using only a small amount of training time and power with our minimal implementation, we were able to see improvement in swarm performance over baseline statistical methods. These results indicate that our technique can be extended to achieve even greater performance results over a wide variety of problem sets. Additionally, this technique can be extended to many problems within the swarm and collaborative multi-agent system space.
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