学习用不确定的拓扑地图进行规划
Learning to plan with uncertain topological maps
摘要：
我们训练的代理在3D环境中使用分层策略，包括一个高层次的基于图形的规划和本地政策。我们的主要贡献是一个数据驱动的学习为基础的方法规划下的不确定性拓扑地图，需要估计的最短路径的概率结构的值图。而经典的符号算法实现无噪声拓扑结构的最佳结果，或在概率意义上的概率结构的图形的最佳结果，我们的目标是表明，机器学习可以克服丢失的信息，在图中考虑到丰富的高维节点功能，例如在地图的每个位置可用的视觉信息。相比纯粹学习神经白盒算法，我们构造我们的神经模型与归纳偏见动态规划为基础的最短路径算法，我们表明，我们的神经模型的一个特定的参数化对应于贝尔曼-福特算法。通过在模拟的照片般逼真的3D环境中对我们的方法进行实证分析，我们证明了在学习的神经规划器中包含视觉特征优于基于图的规划的经典符号解决方案。
英文摘要：
We train an agent to navigate in 3D environments using a hierarchical strategy including a high-level graph based planner and a local policy. Our main contribution is a data driven learning based approach for planning under uncertainty in topological maps, requiring an estimate of shortest paths in valued graphs with a probabilistic structure. Whereas classical symbolic algorithms achieve optimal results on noise-less topologies, or optimal results in a probabilistic sense on graphs with probabilistic structure, we aim to show that machine learning can overcome missing information in the graph by taking into account rich high-dimensional node features, for instance visual information available at each location of the map. Compared to purely learned neural white box algorithms, we structure our neural model with an inductive bias for dynamic programming based shortest path algorithms, and we show that a particular parameterization of our neural model corresponds to the Bellman-Ford algorithm. By performing an empirical analysis of our method in simulated photo-realistic 3D environments, we demonstrate that the inclusion of visual features in the learned neural planner outperforms classical symbolic solutions for graph based planning.
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。
