{"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/sample-efficient-policy-gradient-methods-with","title":"Sample Efficient Policy Gradient Methods with Recursive Variance Reduction","arxiv_id":"1909.08610","date":"2019-09-18","proceeding":"ICLR 2020 1","authors":["Pan Xu","Felicia Gao","Quanquan Gu"],"abstract":"Improving the sample efficiency in reinforcement learning has been a long-standing research problem. In this work, we aim to reduce the sample complexity of existing policy gradient methods. We propose a novel policy gradient algorithm called SRVR-PG, which only requires $O(1/\\epsilon^{3/2})$ episodes to find an $\\epsilon$-approximate stationary point of the nonconcave performance function $J(\\boldsymbol{\\theta})$ (i.e., $\\boldsymbol{\\theta}$ such that $\\|\\nabla J(\\boldsymbol{\\theta})\\|_2^2\\leq\\epsilon$). This sample complexity improves the existing result $O(1/\\epsilon^{5/3})$ for stochastic variance reduced policy gradient algorithms by a factor of $O(1/\\epsilon^{1/6})$. In addition, we also propose a variant of SRVR-PG with parameter exploration, which explores the initial policy parameter from a prior probability distribution. We conduct numerical experiments on classic control problems in reinforcement learning to validate the performance of our proposed algorithms.","url_abs":"https://arxiv.org/abs/1909.08610v3","url_pdf":"https://arxiv.org/pdf/1909.08610v3.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":"sample-efficient-policy-gradient-methods-with","repo_url":"https://github.com/xgfelicia/SRVRPG","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"policy-gradient-methods","task_name":"Policy Gradient Methods"},{"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":"https://app.syntology.ai/?focus=1909.08610","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}