{"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/learning-from-the-memory-of-atari-2600","title":"Learning from the memory of Atari 2600","arxiv_id":"1605.01335","date":"2016-05-04","proceeding":null,"authors":["Jakub Sygnowski","Henryk Michalewski"],"abstract":"We train a number of neural networks to play games Bowling, Breakout and\nSeaquest using information stored in the memory of a video game console Atari\n2600. We consider four models of neural networks which differ in size and\narchitecture: two networks which use only information contained in the RAM and\ntwo mixed networks which use both information in the RAM and information from\nthe screen. As the benchmark we used the convolutional model proposed in NIPS\nand received comparable results in all considered games. Quite surprisingly, in\nthe case of Seaquest we were able to train RAM-only agents which behave better\nthan the benchmark screen-only agent. Mixing screen and RAM did not lead to an\nimproved performance comparing to screen-only and RAM-only agents.","url_abs":"http://arxiv.org/abs/1605.01335v1","url_pdf":"http://arxiv.org/pdf/1605.01335v1.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":"learning-from-the-memory-of-atari-2600","repo_url":"https://github.com/nancyhwr/DQN_Ram","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"learning-from-the-memory-of-atari-2600","repo_url":"https://github.com/ulstu/ml","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"learning-from-the-memory-of-atari-2600","repo_url":"https://github.com/ulstu/robotics_ml","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"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}