{"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/synthesizing-neural-network-controllers-with","title":"Synthesizing Neural Network Controllers with Probabilistic Model based Reinforcement Learning","arxiv_id":"1803.02291","date":"2018-03-06","proceeding":null,"authors":["Juan Camilo Gamboa Higuera","David Meger","Gregory Dudek"],"abstract":"We present an algorithm for rapidly learning controllers for robotics\nsystems. The algorithm follows the model-based reinforcement learning paradigm,\nand improves upon existing algorithms; namely Probabilistic learning in Control\n(PILCO) and a sample-based version of PILCO with neural network dynamics\n(Deep-PILCO). We propose training a neural network dynamics model using\nvariational dropout with truncated Log-Normal noise. This allows us to obtain a\ndynamics model with calibrated uncertainty, which can be used to simulate\ncontroller executions via rollouts. We also describe set of techniques,\ninspired by viewing PILCO as a recurrent neural network model, that are crucial\nto improve the convergence of the method. We test our method on a variety of\nbenchmark tasks, demonstrating data-efficiency that is competitive with PILCO,\nwhile being able to optimize complex neural network controllers. Finally, we\nassess the performance of the algorithm for learning motor controllers for a\nsix legged autonomous underwater vehicle. This demonstrates the potential of\nthe algorithm for scaling up the dimensionality and dataset sizes, in more\ncomplex control tasks.","url_abs":"http://arxiv.org/abs/1803.02291v3","url_pdf":"http://arxiv.org/pdf/1803.02291v3.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":"synthesizing-neural-network-controllers-with","repo_url":"https://github.com/juancamilog/kusanagi","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"synthesizing-neural-network-controllers-with","repo_url":"https://github.com/juancamilog/robot_learning","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"synthesizing-neural-network-controllers-with","repo_url":"https://github.com/mcgillmrl/robot_learning","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"model-based-reinforcement-learning","task_name":"Model-based Reinforcement Learning"},{"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":[{"method_slug":"dropout","method_name":"Dropout"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1803.02291","atlas_url":"https://app.syntology.ai/?focus=1803.02291","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}