广义值迭代网络：格之外的生命
Generalized Value Iteration Networks: Life Beyond Lattices
摘要：
在本文中，我们介绍了一个广义值迭代网络（GVIN），这是一个端到端的神经网络规划模块。GVIN通过使用一种新的图卷积算子来模拟值迭代算法，使GVIN能够在不规则的空间图上学习和规划。我们提出了三种新的可微内核作为图卷积算子，并表明基于嵌入的内核实现了最佳性能。我们进一步提出了情景Q学习，这是对传统n步Q学习的改进，它可以稳定包含规划模块的网络的训练。最后，我们评估GVIN在2D迷宫，不规则图形和现实世界的街道网络规划问题，表明GVIN推广以及任意图形和看不见的图形较大的规模和优于一个天真的推广VIN（离散化的空间图形到一个2D图像）。
英文摘要：
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).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
