{"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/decoupled-data-based-approach-for-learning-to","title":"Decoupled Data Based Approach for Learning to Control Nonlinear Dynamical Systems","arxiv_id":"1904.08361","date":"2019-04-17","proceeding":null,"authors":["Ran Wang","Karthikeya Parunandi","Dan Yu","Dileep Kalathil","Suman Chakravorty"],"abstract":"This paper addresses the problem of learning the optimal control policy for a\nnonlinear stochastic dynamical system with continuous state space, continuous\naction space and unknown dynamics. This class of problems are typically\naddressed in stochastic adaptive control and reinforcement learning literature\nusing model-based and model-free approaches respectively. Both methods rely on\nsolving a dynamic programming problem, either directly or indirectly, for\nfinding the optimal closed loop control policy. The inherent `curse of\ndimensionality' associated with dynamic programming method makes these\napproaches also computationally difficult.\n  This paper proposes a novel decoupled data-based control (D2C) algorithm that\naddresses this problem using a decoupled, `open loop - closed loop', approach.\nFirst, an open-loop deterministic trajectory optimization problem is solved\nusing a black-box simulation model of the dynamical system. Then, a closed loop\ncontrol is developed around this open loop trajectory by linearization of the\ndynamics about this nominal trajectory. By virtue of linearization, a linear\nquadratic regulator based algorithm can be used for this closed loop control.\nWe show that the performance of D2C algorithm is approximately optimal.\nMoreover, simulation performance suggests significant reduction in training\ntime compared to other state of the art algorithms.","url_abs":"http://arxiv.org/abs/1904.08361v1","url_pdf":"http://arxiv.org/pdf/1904.08361v1.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":"decoupled-data-based-approach-for-learning-to","repo_url":"https://github.com/rwang0417/d2c_mujoco200","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"reinforcement-learning","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}