{"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/super-reinforcement-bros-playing-super-mario","title":"Super Reinforcement Bros: Playing Super Mario Bros with Reinforcement Learning","arxiv_id":null,"date":"2020-12-14","proceeding":"CUHK Course IERG5350 2020 12","authors":["Nan Zhang","Zixing Song"],"abstract":"We plan to apply and adjust some well-known reinforcement learning (RL) algorithms to train an automatic agent to play the 1985 Nintendo game Super Mario Bros under a speedrun rule. The agent may learn several control policies from raw pixel data by using deep reinforcement learning. By the end of the project, we expect the model to perform comparably to or top human players in a given stage. \nCode and pre-trained models are available at https://github.com/AnthonySong98/Super-Mario-Bros-PPO. \nThe video recording is available at https://drive.google.com/file/d/1A9d81LUKM9h8Msw9l9Xu2PrpCXnZGgcl/view.","url_abs":"https://openreview.net/forum?id=T5bLw0jDe7Q","url_pdf":"https://openreview.net/pdf?id=T5bLw0jDe7Q","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":"super-reinforcement-bros-playing-super-mario","repo_url":"https://github.com/anthonysong98/super-mario-bros-ppo","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"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}