{"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/investigating-practical-linear-temporal","title":"Investigating practical linear temporal difference learning","arxiv_id":"1602.08771","date":"2016-02-28","proceeding":null,"authors":["Adam White","Martha White"],"abstract":"Off-policy reinforcement learning has many applications including: learning\nfrom demonstration, learning multiple goal seeking policies in parallel, and\nrepresenting predictive knowledge. Recently there has been an proliferation of\nnew policy-evaluation algorithms that fill a longstanding algorithmic void in\nreinforcement learning: combining robustness to off-policy sampling, function\napproximation, linear complexity, and temporal difference (TD) updates. This\npaper contains two main contributions. First, we derive two new hybrid TD\npolicy-evaluation algorithms, which fill a gap in this collection of\nalgorithms. Second, we perform an empirical comparison to elicit which of these\nnew linear TD methods should be preferred in different situations, and make\nconcrete suggestions about practical use.","url_abs":"http://arxiv.org/abs/1602.08771v2","url_pdf":"http://arxiv.org/pdf/1602.08771v2.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":"investigating-practical-linear-temporal","repo_url":"https://github.com/sinaghiassian/OffpolicyAlgorithms","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}