Generalized Value Iteration Networks: Life Beyond Lattices

Autor:	Niu, Sufeng, Chen, Siheng, Guo, Hanyu, Targonski, Colin, Smith, Melissa C., Kovačević, Jelena
Rok vydání:	2017
Předmět:	Computer Science - Learning Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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). Comment: 14 pages, conference
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1706.02416 Zobrazit plný text záznamu View this record from Arxiv