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{ "pk": 29801, "title": "Investigating Simple Object Representations in Model-Free Deep ReinforcementLearning", "subtitle": null, "abstract": "We explore the benefits of augmenting state-of-the-art model-free deep reinforcement learning with simple object representa-tions. Following the Frostbite challenge posited by Lake et al.(2017), we identify object representations as a critical cognitivecapacity lacking from current reinforcement learning agents.We discover that providing the Rainbow model (Hessel et al.,2018) with simple, feature-engineered object representationssubstantially boosts its performance on the Frostbite game fromAtari 2600. We then analyze the relative contributions of therepresentations of different types of objects, identify environ-ment states where these representations are most impactful, andexamine how these representations aid in generalizing to novelsituations.", "language": "eng", "license": { "name": "", "short_name": "", "text": null, "url": "" }, "keywords": [ { "word": "deep reinforcement learning; object representa-tions; model-free reinforcement learning; DQN." } ], "section": "Poster Session 2", "is_remote": true, "remote_url": "https://escholarship.org/uc/item/36c0g2mz", "frozenauthors": [ { "first_name": "Guy", "middle_name": "", "last_name": "Davidson", "name_suffix": "", "institution": "New York University", "department": "" }, { "first_name": "Brenden", "middle_name": "M.", "last_name": "Lake", "name_suffix": "", "institution": "New York University", "department": "" } ], "date_submitted": null, "date_accepted": null, "date_published": "2020-01-01T18:00:00Z", "render_galley": null, "galleys": [ { "label": "PDF", "type": "pdf", "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/29801/galley/19655/download/" } ] }