Improving Model-Based Control and Active Exploration with Reconstruction Uncertainty Optimization

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, sub-optimal 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.