{"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-policy-evaluation-in-reinforcement","title":"UVIP: Model-Free Approach to Evaluate Reinforcement Learning Algorithms","arxiv_id":"2105.02135","date":"2021-05-05","proceeding":null,"authors":["Ilya Levin","Denis Belomestny","Alexey Naumov","Sergey Samsonov"],"abstract":"Policy evaluation is an important instrument for the comparison of different algorithms in Reinforcement Learning (RL). Yet even a precise knowledge of the value function $V^{\\pi}$ corresponding to a policy $\\pi$ does not provide reliable information on how far is the policy $\\pi$ from the optimal one. We present a novel model-free upper value iteration procedure $({\\sf UVIP})$ that allows us to estimate the suboptimality gap $V^{\\star}(x) - V^{\\pi}(x)$ from above and to construct confidence intervals for $V^\\star$. Our approach relies on upper bounds to the solution of the Bellman optimality equation via martingale approach. We provide theoretical guarantees for ${\\sf UVIP}$ under general assumptions and illustrate its performance on a number of benchmark RL problems.","url_abs":"https://arxiv.org/abs/2105.02135v4","url_pdf":"https://arxiv.org/pdf/2105.02135v4.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-policy-evaluation-in-reinforcement","repo_url":"https://github.com/human0being/uvip-rl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"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}