{"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/terrain-rl-simulator","title":"Terrain RL Simulator","arxiv_id":"1804.06424","date":"2018-04-17","proceeding":null,"authors":["Glen Berseth","Xue Bin Peng","Michiel Van de Panne"],"abstract":"We provide $89$ challenging simulation environments that range in difficulty.\nThe difficulty of solving a task is linked not only to the number of dimensions\nin the action space but also to the size and shape of the distribution of\nconfigurations the agent experiences. Therefore, we are releasing a number of\nsimulation environments that include randomly generated terrain. The library\nalso provides simple mechanisms to create new environments with different agent\nmorphologies and the option to modify the distribution of generated terrain. We\nbelieve using these and other more complex simulations will help push the field\ncloser to creating human-level intelligence.","url_abs":"http://arxiv.org/abs/1804.06424v1","url_pdf":"http://arxiv.org/pdf/1804.06424v1.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":"terrain-rl-simulator","repo_url":"https://github.com/Neo-X/TerrainRLSim","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","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}