# Massive Parallel Deep Reinforcement Learning for Active SLAM

arXiv: https://arxiv.org/abs/2603.25834 · HTML: https://mlsanity.com/paper/2603.25834/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 26 Mar 2026.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 8 of 8)

- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [Learning to Explore using Active Neural SLAM](https://mlsanity.com/paper/2004.05155/) (2020)
- [A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers](https://mlsanity.com/paper/2207.00254/) (2022)
- [Goal-Driven Autonomous Exploration Through Deep Reinforcement Learning](https://mlsanity.com/paper/2103.07119/) (2021)
- [Deep Reinforcement Learning based Automatic Exploration for Navigation in Unknown Environment](https://mlsanity.com/paper/2007.11808/) (2020)
- [Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on Graphs](https://mlsanity.com/paper/2007.12640/) (2020)
- [NavRL: Learning Safe Flight in Dynamic Environments](https://mlsanity.com/paper/2409.15634/) (2024)

## Abstract (arXiv)

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.