深度强化学习环境中用于自主搜索与跟踪的 Stone Soup 多目标跟踪特征提取
Stone Soup Multi-Target Tracking Feature Extraction For Autonomous Search And Track In Deep Reinforcement Learning Environment
AI总结：
本文提出利用 Stone Soup 跟踪框架作为特征提取器嵌入 Gymnasium 环境，训练深度强化学习智能体执行传感器管理任务，实验证明该 RL 智能体优于简单搜索与跟踪策略。
摘要：
对于未来的军事空中装备而言，感知资源的管理是一个重要且复杂的问题，未来的系统将部署异构传感器以生成战场空间信息。包括深度强化学习（DRL）在内的机器学习技术已被认为是极具前景的方法，但其需要高保真度的训练环境和特征提取器来为智能体生成信息。本文提出了一种深度强化学习训练方法，利用 Stone Soup 跟踪框架作为特征提取器，训练智能体执行传感器管理任务。本文提出了一个将 Stone Soup 跟踪器组件嵌入 Gymnasium 环境的通用框架，从而能够使用 Stable Baselines3 进行快速且可配置的强化学习训练跟踪器部署。该方法在传感器管理任务中得到了演示，在该任务中，智能体被训练以利用由 Stone Soup 跟踪器生成的航迹列表来搜索和跟踪一片空域。在搜索与跟踪场景下，使用三种神经网络架构的示例实现演示了该方法，并表明在 Gymnasium 和 Stone Soup 环境中训练的强化学习智能体能够优于简单的传感器搜索与跟踪策略。
英文摘要：
Management of sensing resources is a non-trivial problem for future military air assets with future systems deploying heterogeneous sensors to generate information of the battlespace. Machine learning techniques including deep reinforcement learning (DRL) have been identified as promising approaches, but require high-fidelity training environments and feature extractors to generate information for the agent. This paper presents a deep reinforcement learning training approach, utilising the Stone Soup tracking framework as a feature extractor to train an agent for a sensor management task. A general framework for embedding Stone Soup tracker components within a Gymnasium environment is presented, enabling fast and configurable tracker deployments for RL training using Stable Baselines3. The approach is demonstrated in a sensor management task where an agent is trained to search and track a region of airspace utilising track lists generated from Stone Soup trackers. A sample implementation using three neural network architectures in a search-and-track scenario demonstrates the approach and shows that RL agents can outperform simple sensor search and track policies when trained within the Gymnasium and Stone Soup environment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
