UC Berkeley – Keeping Learning-Based Control Safe by Regulating Distributional Shift
To regulate the distribution shift experience by learning-based controllers, we seek a mechanism for constraining the agent to regions of high data density throughout its trajectory (left). Here, we present an approach which achieves this goal by combining features of density models (middle) and Lyapunov functions (right). In order to make use of machine learning…