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.

本文采用一种数据驱动的策略和基于图像的规划，应用于 3D 环境中的导航系统，通过机器学习方法实现了图像哈希表上的最短路径规划，比传统符号算法更优秀。

学习使用不确定拓扑地图进行规划

Learning to plan with uncertain topological maps

In the following article we consider approximate Bayesian computation (ABC)
for certain classes of time series models. In particular, we focus upon
scenarios where the likelihoods of the observations and parameter are
intractable, by which we mean that one cannot evaluate the likelihood even
up-to a positive unbiased estimate. This paper reviews and develops a class of
approximation procedures based upon the idea of ABC, but, specifically
maintains the probabilistic structure of the original statistical model. This
idea is useful, in that it can facilitate an analysis of the bias of the
approximation and the adaptation of established computational methods for
parameter inference. Several existing results in the literature are surveyed
and novel developments with regards to computation are given.

本文介绍了一类基于 ABC 思路的近似方法，着重于处理那些观测和参数似然不易求得的时间序列模型，并保持了原始统计模型的概率结构，为参数推断提供了一种分析近似偏差和适应计算方法的新途径。