Massive Parallel Deep Reinforcement Learning for Active SLAM

2026-03-26

AI Summary

This work proposes the first large-scale, parallel, end-to-end deep reinforcement learning framework tailored for active SLAM, addressing key limitations in existing approaches regarding scalable parallel training, modeling of continuous action spaces, and adaptability to complex real-world environments. By integrating GPU acceleration with an efficient distributed architecture, the framework enables continuous control while substantially improving training efficiency and generalization capability. Compared to current methods, it achieves significantly reduced training time and superior performance in high-fidelity, complex scenarios. To foster reproducibility and community adoption, the authors publicly release the source code alongside the framework.

Technology Category

Machine Learning: Reinforcement LearningSearch and Optimization: Learning to SearchMultiagent Systems: Multiagent Learning

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingGraph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphsSystems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Active SLAM
• Deep Reinforcement Learning
• Parallel Training
• Scalability
• GPU Acceleration

Innovation

• Massively Parallel Training
• Deep Reinforcement Learning
• Active SLAM
• Continuous Action Spaces
• Scalable Framework