{"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/beating-the-worlds-best-at-super-smash-bros","title":"Beating the World's Best at Super Smash Bros. with Deep Reinforcement Learning","arxiv_id":"1702.06230","date":"2017-02-21","proceeding":null,"authors":["Vlad Firoiu","William F. Whitney","Joshua B. Tenenbaum"],"abstract":"There has been a recent explosion in the capabilities of game-playing\nartificial intelligence. Many classes of RL tasks, from Atari games to motor\ncontrol to board games, are now solvable by fairly generic algorithms, based on\ndeep learning, that learn to play from experience with minimal knowledge of the\nspecific domain of interest. In this work, we will investigate the performance\nof these methods on Super Smash Bros. Melee (SSBM), a popular console fighting\ngame. The SSBM environment has complex dynamics and partial observability,\nmaking it challenging for human and machine alike. The multi-player aspect\nposes an additional challenge, as the vast majority of recent advances in RL\nhave focused on single-agent environments. Nonetheless, we will show that it is\npossible to train agents that are competitive against and even surpass human\nprofessionals, a new result for the multi-player video game setting.","url_abs":"http://arxiv.org/abs/1702.06230v3","url_pdf":"http://arxiv.org/pdf/1702.06230v3.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":"beating-the-worlds-best-at-super-smash-bros","repo_url":"https://github.com/shaneallcroft/SSB64RLBot","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"atari-games","task_name":"Atari Games"},{"task_slug":"board-games","task_name":"Board Games"},{"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":{"atlas_url":"https://app.syntology.ai/?focus=1702.06230","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}