提出可大规模并行训练的强化学习框架，加速主动建图求解。
Massive Parallel Deep Reinforcement Learning for Active SLAM
- 采用端到端深度强化学习，支持海量并行训练
- 训练时间显著缩短，兼容连续动作空间
- 开源发布，适合研究实时主动建图的开发者
近年来，并行计算与GPU加速为计算密集型学习问题带来了新机遇，例如主动同时定位与建图（Active SLAM）——通过选择动作来降低不确定性并提升地图构建与定位性能。然而，现有基于深度强化学习（DRL）的方法受限于缺乏可扩展的并行训练能力。本文提出一种可扩展的端到端DRL框架，实现主动SLAM的海量并行训练。相比现有方法，该框架显著缩短训练时间，支持连续动作空间，并能探索更真实的场景。代码已开源，旨在促进可复现性与社区应用。
原文摘要 · Abstract (English)
Recent advances in parallel computing and GPU acceleration have created new opportunities for computation-intensive learning problems such as Active SLAM -- where actions are selected to reduce uncertainty and improve joint mapping and localization. However, existing DRL-based approaches remain constrained by the lack of scalable parallel training. In this work, we address this challenge by proposing a scalable end-to-end DRL framework for Active SLAM that enables massively parallel training. Compared with the state of the art, our method significantly reduces training time, supports continuous action spaces and facilitates the exploration of more realistic scenarios. It is released as an open-source framework to promote reproducibility and community adoption.
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