mlsanityData to 14 Oct 2026

Massive Parallel Deep Reinforcement Learning for Active SLAM

arXiv 2603.25834 ↗First posted 26 Mar 2026cs.ROcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

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.

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.

Builds on

8 of 24 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024NavRL: Learning Safe Flight in Dynamic Environments24 Sep

Other references · 7

  1. 2017A Brief Survey of Deep Reinforcement Learningcited by 483
  2. 2016Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Agecited by 411
  3. 2020Learning to Explore using Active Neural SLAMcited by 316
  4. 2022A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontierscited by 66
  5. 2021Goal-Driven Autonomous Exploration Through Deep Reinforcement Learningcited by 35
  6. 2020Deep Reinforcement Learning based Automatic Exploration for Navigation in Unknown Environmentcited by 34
  7. 2020Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on Graphscited by 30

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  1. 2025MA-SLAM: Active SLAM in Large-Scale Unknown Environment using Map Aware Deep Reinforcement Learning
  2. 2021Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning
  3. 2024Deep Reinforcement Learning-based Large-scale Robot Exploration
  4. 2024Optimal Parallelization Strategies for Active Flow Control in Deep Reinforcement Learning-Based Computational Fluid Dynamics
  5. 2024A Survey on Reinforcement Learning Applications in SLAM
  6. 2024Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning
  7. 2025Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks
  8. 2026FlashNav: Training Deployable Robot Navigation Policies in Seconds