Title: Generalized Value Iteration Networks: Life Beyond Lattices,
Abstract: 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).