{"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/crawling-in-rogues-dungeons-with-partitioned","title":"Crawling in Rogue's dungeons with (partitioned) A3C","arxiv_id":"1804.08685","date":"2018-04-23","proceeding":null,"authors":["Andrea Asperti","Daniele Cortesi","Francesco Sovrano"],"abstract":"Rogue is a famous dungeon-crawling video-game of the 80ies, the ancestor of\nits gender. Rogue-like games are known for the necessity to explore partially\nobservable and always different randomly-generated labyrinths, preventing any\nform of level replay. As such, they serve as a very natural and challenging\ntask for reinforcement learning, requiring the acquisition of complex,\nnon-reactive behaviors involving memory and planning. In this article we show\nhow, exploiting a version of A3C partitioned on different situations, the agent\nis able to reach the stairs and descend to the next level in 98% of cases.","url_abs":"http://arxiv.org/abs/1804.08685v3","url_pdf":"http://arxiv.org/pdf/1804.08685v3.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":"crawling-in-rogues-dungeons-with-partitioned","repo_url":"https://github.com/Francesco-Sovrano/Partitioned-A3C-for-RogueInABox","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"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":[{"method_slug":"a3c","method_name":"A3C"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"entropy-regularization","method_name":"Entropy Regularization"},{"method_slug":"softmax","method_name":"Softmax"}],"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}