2018/12/10 by Norman Di Palo, Di Palo, Norman, Harri Valpola +1 · 1 citation
Engineering · #Advanced Control Systems Optimization #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural and Evolutionary Computing (cs.NE) #Reservoir Engineering and Simulation Methods #Robotics (cs.RO) #Space Satellite Systems and Control
paper · pdf · doi:10.48550/arxiv.1812.03955
openalex publication_date 2018/12/10 · openalex created_date 2018/12/22 · openalex updated_date 2026/07/28
Model based predictions of future trajectories of a dynamical system often suffer from inaccuracies, forcing model based control algorithms to re-plan often, thus being computationally expensive, suboptimal and not reliable. In this work, we propose a model agnostic method for estimating the uncertainty of a model?s predictions based on reconstruction error, using it in control and exploration. As our experiments show, this uncertainty estimation can be used to improve control performance on a wide variety of environments by choosing predictions of which the model is confident. It can also be used for active learning to explore more efficiently the environment by planning for trajectories with high uncertainty, allowing faster model learning.