{"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/comparison-of-reinforcement-learning","title":"Comparison of Reinforcement Learning algorithms applied to the Cart Pole problem","arxiv_id":"1810.01940","date":"2018-10-03","proceeding":null,"authors":["Savinay Nagendra","Nikhil Podila","Rashmi Ugarakhod","Koshy George"],"abstract":"Designing optimal controllers continues to be challenging as systems are\nbecoming complex and are inherently nonlinear. The principal advantage of\nreinforcement learning (RL) is its ability to learn from the interaction with\nthe environment and provide optimal control strategy. In this paper, RL is\nexplored in the context of control of the benchmark cartpole dynamical system\nwith no prior knowledge of the dynamics. RL algorithms such as\ntemporal-difference, policy gradient actor-critic, and value function\napproximation are compared in this context with the standard LQR solution.\nFurther, we propose a novel approach to integrate RL and swing-up controllers.","url_abs":"http://arxiv.org/abs/1810.01940v1","url_pdf":"http://arxiv.org/pdf/1810.01940v1.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":"comparison-of-reinforcement-learning","repo_url":"https://github.com/n1shetty/Cart-Pole-Balance-with-Reinforcement-Learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","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":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}