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.

翻译：当前强化学习在解决探索难题时经常面临困难，即期望结果或高奖励很少被观测到。尽管课程强化学习通过提出一系列代理任务来解决复杂任务的框架展示了合理效果，但多数先前工作因缺乏无需领域先验知识即可获得对期望结果状态校准引导的机制，仍难以提出有效的课程。为解决这一问题，我们提出了一种基于不确定性与时域距离感知的课程目标生成方法，用于结果导向强化学习，通过求解二分匹配问题实现。该方法不仅能提供精确校准的课程引导至期望结果状态，相比先前课程强化学习方法，还带来了更优的样本效率以及与几何无关的课程目标提议能力。我们通过定量与定性方式证明，该算法在多种具有挑战性的导航任务和机器人操作任务中显著优于这些先前方法。