{"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/neural-network-dynamics-for-model-based-deep","title":"Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning","arxiv_id":"1708.02596","date":"2017-08-08","proceeding":null,"authors":["Anusha Nagabandi","Gregory Kahn","Ronald S. Fearing","Sergey Levine"],"abstract":"Model-free deep reinforcement learning algorithms have been shown to be\ncapable of learning a wide range of robotic skills, but typically require a\nvery large number of samples to achieve good performance. Model-based\nalgorithms, in principle, can provide for much more efficient learning, but\nhave proven difficult to extend to expressive, high-capacity models such as\ndeep neural networks. In this work, we demonstrate that medium-sized neural\nnetwork models can in fact be combined with model predictive control (MPC) to\nachieve excellent sample complexity in a model-based reinforcement learning\nalgorithm, producing stable and plausible gaits to accomplish various complex\nlocomotion tasks. We also propose using deep neural network dynamics models to\ninitialize a model-free learner, in order to combine the sample efficiency of\nmodel-based approaches with the high task-specific performance of model-free\nmethods. We empirically demonstrate on MuJoCo locomotion tasks that our pure\nmodel-based approach trained on just random action data can follow arbitrary\ntrajectories with excellent sample efficiency, and that our hybrid algorithm\ncan accelerate model-free learning on high-speed benchmark tasks, achieving\nsample efficiency gains of 3-5x on swimmer, cheetah, hopper, and ant agents.\nVideos can be found at https://sites.google.com/view/mbmf","url_abs":"http://arxiv.org/abs/1708.02596v2","url_pdf":"http://arxiv.org/pdf/1708.02596v2.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":"neural-network-dynamics-for-model-based-deep","repo_url":"https://github.com/MVerghese/NN-Dynamics-Sparse-Reward","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"neural-network-dynamics-for-model-based-deep","repo_url":"https://github.com/Qtsho/model-based-rl-navigation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"neural-network-dynamics-for-model-based-deep","repo_url":"https://github.com/anagabandi/nn_dynamics","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"neural-network-dynamics-for-model-based-deep","repo_url":"https://github.com/aravind0706/neural-mpc","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"neural-network-dynamics-for-model-based-deep","repo_url":"https://github.com/aravindsrinivas/neural-mpc","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"neural-network-dynamics-for-model-based-deep","repo_url":"https://github.com/mehdimashayekhi/Some-RL-Implementation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"neural-network-dynamics-for-model-based-deep","repo_url":"https://github.com/nagaban2/nn_dynamics","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"neural-network-dynamics-for-model-based-deep","repo_url":"https://github.com/natolambert/dynamicslearn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"neural-network-dynamics-for-model-based-deep","repo_url":"https://github.com/MindCode-4/code-8/tree/main/Neural-Network-based-Speech-Enhancement","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"model-predictive-control","task_name":"Model Predictive Control"},{"task_slug":"model-based-reinforcement-learning","task_name":"Model-based Reinforcement Learning"},{"task_slug":"mujoco","task_name":"MuJoCo"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"model","task_name":"model"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1708.02596","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1708.02596"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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