Current reinforcement learning (RL) often suffers when solving a challenging
exploration problem where the desired outcomes or high rewards are rarely
observed. Even though curriculum RL, a framework that solves complex tasks by
proposing a sequence of surrogate tasks, shows reasonable results, most of the
previous works still have difficulty in proposing curriculum due to the absence
of a mechanism for obtaining calibrated guidance to the desired outcome state
without any prior domain knowledge. To alleviate it, we propose an uncertainty
& temporal distance-aware curriculum goal generation method for the
outcome-directed RL via solving a bipartite matching problem. It could not only
provide precisely calibrated guidance of the curriculum to the desired outcome
states but also bring much better sample efficiency and geometry-agnostic
curriculum goal proposal capability compared to previous curriculum RL methods.
We demonstrate that our algorithm significantly outperforms these prior methods
in a variety of challenging navigation tasks and robotic manipulation tasks in
a quantitative and qualitative way.

本文提出了一种针对增强学习的不确定性和时间距离感知课程目标生成方法，通过解决二分图匹配问题，为课程提供精确的指导，从而更好地解决了先前课程 RL 方法中存在的问题，并在数量和质量上显著优于这些方法。