mlsanityData to 14 Oct 2026

Parallel Distributional Deep Reinforcement Learning for Mapless Navigation of Terrestrial Mobile Robots

arXiv 2408.05744 ↗First posted 11 Aug 2024cs.ROcited by 0

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

Abstract (arXiv)

This paper introduces novel deep reinforcement learning (Deep-RL) techniques using parallel distributional actor-critic networks for navigating terrestrial mobile robots. Our approaches use laser range findings, relative distance, and angle to the target to guide the robot. We trained agents in the Gazebo simulator and deployed them in real scenarios. Results show that parallel distributional Deep-RL algorithms enhance decision-making and outperform non-distributional and behavior-based approaches in navigation and spatial generalization.

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 14 of its 21 references that are in the map. First posted on arXiv 11 Aug 2024.

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14 of 21 references.

References · 14

  1. 2015Continuous control with deep reinforcement learningcited by 3,407
  2. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  3. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
  4. 2015Prioritized Experience Replaycited by 1,099
  5. 2017A Distributional Perspective on Reinforcement Learningcited by 765
  6. 2016Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learningcited by 366
  7. 2018Distributed Prioritized Experience Replaycited by 269
  8. 2018Distributed Distributional Deterministic Policy Gradientscited by 199
  9. 2016Continuous Deep Q-Learning with Model-based Accelerationcited by 157
  10. 2017Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigationcited by 98
  11. 2020Distributional Soft Actor-Critic: Off-Policy Reinforcement Learning for Addressing Value Estimation Errorscited by 85
  12. 2016Towards Cognitive Exploration through Deep Reinforcement Learning for Mobile Robotscited by 11
2 more
  1. 2021Deep Reinforcement Learning for Mapless Navigation of a Hybrid Aerial Underwater Vehicle with Medium Transitioncited by 9
  2. 2021Double Critic Deep Reinforcement Learning for Mapless 3D Navigation of Unmanned Aerial Vehiclescited by 8

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  1. 2024Deep Reinforcement Learning with Enhanced PPO for Safe Mobile Robot Navigation
  2. 2025The Emergence of Deep Reinforcement Learning for Path Planning
  3. 2024Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes
  4. 2024Deep Reinforcement Learning-based Large-scale Robot Exploration
  5. 2024Mobile Robot Exploration Without Maps via Out-of-Distribution Deep Reinforcement Learning
  6. 2025Taxonomy and Trends in Reinforcement Learning for Robotics and Control Systems: A Structured Review
  7. 2024Improving Generalization in Aerial and Terrestrial Mobile Robots Control Through Delayed Policy Learning
  8. 2025Socially aware navigation for mobile robots: a survey on deep reinforcement learning approaches