{"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/reproducibility-of-benchmarked-deep","title":"Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control","arxiv_id":"1708.04133","date":"2017-08-10","proceeding":null,"authors":["Riashat Islam","Peter Henderson","Maziar Gomrokchi","Doina Precup"],"abstract":"Policy gradient methods in reinforcement learning have become increasingly\nprevalent for state-of-the-art performance in continuous control tasks. Novel\nmethods typically benchmark against a few key algorithms such as deep\ndeterministic policy gradients and trust region policy optimization. As such,\nit is important to present and use consistent baselines experiments. However,\nthis can be difficult due to general variance in the algorithms,\nhyper-parameter tuning, and environment stochasticity. We investigate and\ndiscuss: the significance of hyper-parameters in policy gradients for\ncontinuous control, general variance in the algorithms, and reproducibility of\nreported results. We provide guidelines on reporting novel results as\ncomparisons against baseline methods such that future researchers can make\ninformed decisions when investigating novel methods.","url_abs":"http://arxiv.org/abs/1708.04133v1","url_pdf":"http://arxiv.org/pdf/1708.04133v1.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":"reproducibility-of-benchmarked-deep","repo_url":"https://github.com/Breakend/ReproducibilityInContinuousPolicyGradientMethods","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"continuous-control","task_name":"Continuous Control"},{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"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":"continuous-control","task_name":"continuous-control"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1708.04133","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}