{"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/gym-gazebo2-a-toolkit-for-reinforcement","title":"gym-gazebo2, a toolkit for reinforcement learning using ROS 2 and Gazebo","arxiv_id":"1903.06278","date":"2019-03-14","proceeding":null,"authors":["Nestor Gonzalez Lopez","Yue Leire Erro Nuin","Elias Barba Moral","Lander Usategui San Juan","Alejandro Solano Rueda","Víctor Mayoral Vilches","Risto Kojcev"],"abstract":"This paper presents an upgraded, real world application oriented version of\ngym-gazebo, the Robot Operating System (ROS) and Gazebo based Reinforcement\nLearning (RL) toolkit, which complies with OpenAI Gym. The content discusses\nthe new ROS 2 based software architecture and summarizes the results obtained\nusing Proximal Policy Optimization (PPO). Ultimately, the output of this work\npresents a benchmarking system for robotics that allows different techniques\nand algorithms to be compared using the same virtual conditions. We have\nevaluated environments with different levels of complexity of the Modular\nArticulated Robotic Arm (MARA), reaching accuracies in the millimeter scale.\nThe converged results show the feasibility and usefulness of the gym-gazebo 2\ntoolkit, its potential and applicability in industrial use cases, using modular\nrobots.","url_abs":"http://arxiv.org/abs/1903.06278v2","url_pdf":"http://arxiv.org/pdf/1903.06278v2.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":"abstracts"},"code_links":[{"paper_slug":"gym-gazebo2-a-toolkit-for-reinforcement","repo_url":"https://github.com/AcutronicRobotics/gym-gazebo2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"openai-gym","task_name":"OpenAI Gym"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"transfer-reinforcement-learning","task_name":"Transfer Reinforcement Learning"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}