{"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/mdp-environments-for-the-openai-gym","title":"MDP environments for the OpenAI Gym","arxiv_id":"1709.09069","date":"2017-09-26","proceeding":null,"authors":["Andreas Kirsch"],"abstract":"The OpenAI Gym provides researchers and enthusiasts with simple to use\nenvironments for reinforcement learning. Even the simplest environment have a\nlevel of complexity that can obfuscate the inner workings of RL approaches and\nmake debugging difficult. This whitepaper describes a Python framework that\nmakes it very easy to create simple Markov-Decision-Process environments\nprogrammatically by specifying state transitions and rewards of deterministic\nand non-deterministic MDPs in a domain-specific language in Python. It then\npresents results and visualizations created with this MDP framework.","url_abs":"http://arxiv.org/abs/1709.09069v1","url_pdf":"http://arxiv.org/pdf/1709.09069v1.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":"mdp-environments-for-the-openai-gym","repo_url":"https://github.com/BlackHC/mdp","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"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":"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}