{"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/fast-deep-reinforcement-learning-using-online","title":"Fast deep reinforcement learning using online adjustments from the past","arxiv_id":"1810.08163","date":"2018-10-18","proceeding":"NeurIPS 2018 12","authors":["Steven Hansen","Pablo Sprechmann","Alexander Pritzel","André Barreto","Charles Blundell"],"abstract":"We propose Ephemeral Value Adjusments (EVA): a means of allowing deep\nreinforcement learning agents to rapidly adapt to experience in their replay\nbuffer. EVA shifts the value predicted by a neural network with an estimate of\nthe value function found by planning over experience tuples from the replay\nbuffer near the current state. EVA combines a number of recent ideas around\ncombining episodic memory-like structures into reinforcement learning agents:\nslot-based storage, content-based retrieval, and memory-based planning. We show\nthat EVAis performant on a demonstration task and Atari games.","url_abs":"http://arxiv.org/abs/1810.08163v1","url_pdf":"http://arxiv.org/pdf/1810.08163v1.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":"fast-deep-reinforcement-learning-using-online","repo_url":"https://github.com/AnnaNikitaRL/EVA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"fast-deep-reinforcement-learning-using-online","repo_url":"https://github.com/deepmind/open_spiel","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"atari-games","task_name":"Atari Games"},{"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)"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1810.08163","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.08163"}},"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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