{"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/deep-reinforcement-learning-for-playing-25d","title":"Deep Reinforcement Learning for Playing 2.5D Fighting Games","arxiv_id":"1805.02070","date":"2018-05-05","proceeding":null,"authors":["Yu-Jhe Li","Hsin-Yu Chang","Yu-Jing Lin","Po-Wei Wu","Yu-Chiang Frank Wang"],"abstract":"Deep reinforcement learning has shown its success in game playing. However,\n2.5D fighting games would be a challenging task to handle due to ambiguity in\nvisual appearances like height or depth of the characters. Moreover, actions in\nsuch games typically involve particular sequential action orders, which also\nmakes the network design very difficult. Based on the network of Asynchronous\nAdvantage Actor-Critic (A3C), we create an OpenAI-gym-like gaming environment\nwith the game of Little Fighter 2 (LF2), and present a novel A3C+ network for\nlearning RL agents. The introduced model includes a Recurrent Info network,\nwhich utilizes game-related info features with recurrent layers to observe\ncombo skills for fighting. In the experiments, we consider LF2 in different\nsettings, which successfully demonstrates the use of our proposed model for\nlearning 2.5D fighting games.","url_abs":"http://arxiv.org/abs/1805.02070v1","url_pdf":"http://arxiv.org/pdf/1805.02070v1.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":"deep-reinforcement-learning-for-playing-25d","repo_url":"https://github.com/elvisyjlin/lf2gym","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"deep-reinforcement-learning-for-playing-25d","repo_url":"https://github.com/JIElite/AI-research-papers","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"deep-reinforcement-learning-for-playing-25d","repo_url":"https://github.com/TobiKick/DL_LF2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"deep-reinforcement-learning-for-playing-25d","repo_url":"https://github.com/acht7111020/DRL-playing-2.5D-fighting-game","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"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":[{"method_slug":"info","method_name":"INFO"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}