{"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/model-free-episodic-control","title":"Model-Free Episodic Control","arxiv_id":"1606.04460","date":"2016-06-14","proceeding":null,"authors":["Charles Blundell","Benigno Uria","Alexander Pritzel","Yazhe Li","Avraham Ruderman","Joel Z. Leibo","Jack Rae","Daan Wierstra","Demis Hassabis"],"abstract":"State of the art deep reinforcement learning algorithms take many millions of\ninteractions to attain human-level performance. Humans, on the other hand, can\nvery quickly exploit highly rewarding nuances of an environment upon first\ndiscovery. In the brain, such rapid learning is thought to depend on the\nhippocampus and its capacity for episodic memory. Here we investigate whether a\nsimple model of hippocampal episodic control can learn to solve difficult\nsequential decision-making tasks. We demonstrate that it not only attains a\nhighly rewarding strategy significantly faster than state-of-the-art deep\nreinforcement learning algorithms, but also achieves a higher overall reward on\nsome of the more challenging domains.","url_abs":"http://arxiv.org/abs/1606.04460v1","url_pdf":"http://arxiv.org/pdf/1606.04460v1.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":"model-free-episodic-control","repo_url":"https://github.com/Kaixhin/EC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"model-free-episodic-control","repo_url":"https://github.com/ShibiHe/Model-Free-Episodic-Control","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"model-free-episodic-control","repo_url":"https://github.com/yoojungsun0/Psych239","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":null,"task_name":"Hippocampus"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"sequential-decision-making","task_name":"Sequential Decision Making"},{"task_slug":"model","task_name":"model"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[{"method_slug":"mfec","method_name":"MFEC"}],"datasets_introduced":[],"methods_introduced":[{"slug":"mfec","name":"MFEC","full_name":"Model-Free Episodic Control"}],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1606.04460","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1606.04460"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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