{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/parallel-distributional-deep-reinforcement","title":"Parallel Distributional Deep Reinforcement Learning for Mapless Navigation of Terrestrial Mobile Robots","arxiv_id":"2408.05744","date":"2024-08-11","proceeding":null,"authors":["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"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2408.05744v2","url_pdf":"https://arxiv.org/pdf/2408.05744v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"parallel-distributional-deep-reinforcement","repo_url":"https://github.com/victorkich/Parallel-Turtle-DRL","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}