{"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/improving-reinforcement-learning-algorithms","title":"Improving reinforcement learning algorithms: towards optimal learning rate policies","arxiv_id":"1911.02319","date":"2019-11-06","proceeding":null,"authors":["Othmane Mounjid","Charles-Albert Lehalle"],"abstract":"This paper investigates to what extent one can improve reinforcement learning algorithms. Our study is split in three parts. First, our analysis shows that the classical asymptotic convergence rate $O(1/\\sqrt{N})$ is pessimistic and can be replaced by $O((\\log(N)/N)^{\\beta})$ with $\\frac{1}{2}\\leq \\beta \\leq 1$ and $N$ the number of iterations. Second, we propose a dynamic optimal policy for the choice of the learning rate $(\\gamma_k)_{k\\geq 0}$ used in stochastic approximation (SA). We decompose our policy into two interacting levels: the inner and the outer level. In the inner level, we present the \\nameref{Alg:v_4_s} algorithm (for \"PAst Sign Search\") which, based on a predefined sequence $(\\gamma^o_k)_{k\\geq 0}$, constructs a new sequence $(\\gamma^i_k)_{k\\geq 0}$ whose error decreases faster. In the outer level, we propose an optimal methodology for the selection of the predefined sequence $(\\gamma^o_k)_{k\\geq 0}$. Third, we show empirically that our selection methodology of the learning rate outperforms significantly standard algorithms used in reinforcement learning (RL) in the three following applications: the estimation of a drift, the optimal placement of limit orders and the optimal execution of large number of shares.","url_abs":"https://arxiv.org/abs/1911.02319v6","url_pdf":"https://arxiv.org/pdf/1911.02319v6.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":"improving-reinforcement-learning-algorithms","repo_url":"https://github.com/othmaneM/RL_adap_stepsize","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"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":"https://app.syntology.ai/?focus=1911.02319","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}