Massive Parallel Deep Reinforcement Learning for Active SLAM

TL;DR

This paper introduces a scalable deep reinforcement learning framework for Active SLAM that leverages parallel computing and GPU acceleration to significantly reduce training time and support more realistic scenarios.

Contribution

The authors present a novel end-to-end DRL framework for Active SLAM enabling massively parallel training and supporting continuous action spaces.

Findings

Significantly reduced training time compared to previous methods.

Supports continuous action spaces for more realistic applications.

Open-source framework to promote reproducibility and community use.

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.