{"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/high-performance-on-atari-games-using","title":"High Performance on Atari Games Using Perceptual Control Architecture Without Training","arxiv_id":null,"date":"2022-10-08","proceeding":"Journal of Intelligent and Robotic Systems 2022 10","authors":["Tauseef Gulrez","Warren Mansell"],"abstract":"Deep reinforcement learning (DRL) requires large samples and a long training time to operate optimally. Yet humans rarely\r\nrequire long periods of training to perform well on novel tasks, such as computer games, once they are provided with an\r\naccurate program of instructions. We used perceptual control theory (PCT) to construct a simple closed-loop model which\r\nrequires no training samples and training time within a video game study using the Arcade Learning Environment (ALE).\r\nThe model was programmed to parse inputs from the environment into hierarchically organised perceptual signals, and it\r\ncomputed a dynamic error signal by subtracting the incoming signal for each perceptual variable from a reference signal to\r\ndrive output signals to reduce this error. We tested the same model across three different Atari games Breakout, Pong and\r\nVideo Pinball to achieve performance at least as high as DRL paradigms, and close to good human performance. Our study\r\nshows that perceptual control models, based on simple assumptions, can perform well without learning. We conclude by\r\nspecifying a parsimonious role of learning that may be more similar to psychological functioning.","url_abs":"https://link.springer.com/article/10.1007/s10846-022-01747-5","url_pdf":"https://link.springer.com/content/pdf/10.1007/s10846-022-01747-5.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":"high-performance-on-atari-games-using","repo_url":"https://github.com/PCT-Models/PCTagent_Breakout_Atari","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"}],"methods":[{"method_slug":"pct","method_name":"PCT"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}