Article Instance

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{
    "pk": 31770,
    "title": "Thinking Locally to Act Globally: A Novel Approach to Reinforcement Learning",
    "subtitle": null,
    "abstract": "Reinforcement Learning methods address the prob-\nlem faced by an agent w h o must choose actions in\nan u n k n o w n environment so as to maximize the re-\nwards it receives in return. T o date, the available\ntechniques have relied on temporal discounting, a\nproblematic practice of valuing immediate rewards\nmore heavily than future rewards, or else have im-\nposed strong restrictions on the environment. This\npaper sketches a n e w method which utilizes a subjec-\ntive evaluator of performance in order to (1) choose\nactions that maximize undiscounted rewards and (2)\ndo so at a computational advantage with respect to\nprevious discounted techniques. W e present initial\nexperimental results that attest to a substantial im-\nprovement in performance.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [],
    "section": "Submitted Presentations",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/91f167cn",
    "frozenauthors": [
        {
            "first_name": "Anton",
            "middle_name": "",
            "last_name": "Schwartz",
            "name_suffix": "",
            "institution": "Stanford University",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "1993-01-01T18:00:00Z",
    "render_galley": null,
    "galleys": [
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/31770/galley/22838/download/"
        }
    ]
}