Papers › Parallel Distributional Deep Reinforcement Learning for Mapless Navigation of...
Parallel Distributional Deep Reinforcement Learning for Mapless Navigation of Terrestrial Mobile Robots
Victor Augusto Kich, Alisson Henrique Kolling, Junior Costa de Jesus, Gabriel V. Heisler, Hiago Jacobs, Jair Augusto Bottega, André L. da S. Kelbouscas, Akihisa Ohya, Ricardo Bedin Grando, Paulo Lilles Jorge Drews-Jr, Daniel Fernando Tello Gamarra
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
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.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections