{"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/random-projection-in-neural-episodic-control","title":"Random Projection in Neural Episodic Control","arxiv_id":"1904.01790","date":"2019-04-03","proceeding":null,"authors":["Daichi Nishio","Satoshi Yamane"],"abstract":"End-to-end deep reinforcement learning has enabled agents to learn with\nlittle preprocessing by humans. However, it is still difficult to learn stably\nand efficiently because the learning method usually uses a nonlinear function\napproximation. Neural Episodic Control (NEC), which has been proposed in order\nto improve sample efficiency, is able to learn stably by estimating action\nvalues using a non-parametric method. In this paper, we propose an architecture\nthat incorporates random projection into NEC to train with more stability. In\naddition, we verify the effectiveness of our architecture by Atari's five\ngames. The main idea is to reduce the number of parameters that have to learn\nby replacing neural networks with random projection in order to reduce\ndimensions while keeping the learning end-to-end.","url_abs":"http://arxiv.org/abs/1904.01790v2","url_pdf":"http://arxiv.org/pdf/1904.01790v2.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":"random-projection-in-neural-episodic-control","repo_url":"https://github.com/dnishio/NEC-RP","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}