{"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/solving-atari-games-using-fractals-and","title":"Solving Atari Games Using Fractals And Entropy","arxiv_id":"1807.01081","date":"2018-07-03","proceeding":null,"authors":["Sergio Hernandez Cerezo","Guillem Duran Ballester","Spiros Baxevanakis"],"abstract":"In this paper, we introduce a novel MCTS based approach that is derived from\nthe laws of the thermodynamics. The algorithm coined Fractal Monte Carlo (FMC),\nallows us to create an agent that takes intelligent actions in both continuous\nand discrete environments while providing control over every aspect of the\nagent behavior. Results show that FMC is several orders of magnitude more\nefficient than similar techniques, such as MCTS, in the Atari games tested.","url_abs":"http://arxiv.org/abs/1807.01081v1","url_pdf":"http://arxiv.org/pdf/1807.01081v1.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":"solving-atari-games-using-fractals-and","repo_url":"https://github.com/FragileTheory/FractalAI","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"atari-games","task_name":"Atari Games"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}