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Generalized Value Iteration Network (GVIN) In this paper, we introduce a generalized value iteration network (GVIN), which is an end-to-end neural network planning module. GVIN emulates the value iteration algorithm by using a novel graph convolution operator, which enables GVIN to learn and plan on irregular spatial graphs. We propose three novel differentiable kernels as graph convolution operators and show that the embedding based kernel achieves the best performance. We further propose episodic Q-learning, an improvement upon traditional n-step Q-learning that stabilizes training for networks that contain a planning module. Lastly, we evaluate GVIN on planning problems in 2D mazes, irregular graphs, and real-world street networks, showing that GVIN generalizes well for both arbitrary graphs and unseen graphs of larger scale and outperforms a naive generalization of VIN (discretizing a spatial graph into a 2D image). …

Integral of B-Spline Functions (ibs)Calculate B-spline basis functions with a given set of knots and order, or a B-spline function with a given set of knots and order and set of de Boor p …

In this post, we will focus on the penalty.factor option.

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When it comes to relative newcomers in the Data Science field, there aren’t many out there doing better than Alyssa Columbus. Although she just graduated from college earlier this year, she already has a full-time data scientist role at Pacific Life, a laundry list of conference and symposium speaking engagements, and has founded a local group for women who code in R. Oh, and she’s a NASA Datanaut.