AAAI Publications, Thirty-Second AAAI Conference on Artificial Intelligence

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Generalized Value Iteration Networks:Life Beyond Lattices
Sufeng Niu, Siheng Chen, Hanyu Guo, Colin Targonski, Melissa C. Smith, Jelena Kovačević

Last modified: 2018-04-26

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. Furthermore, we present episodic Q-learning, an improvement upon traditional n-step Q-learning that stabilizes training for VIN and GVIN. 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 scaleand outperforms a naive generalization of VIN (discretizing a spatial graph into a 2D image).

Keywords


value iteration network; irregular graph; graph convolution

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