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 and reinforcement learning in controlling real world systems, we must design algorithms which not only achieve good performance, but also interact with the system in …

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