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目标困难且期望结果或高奖励极少观测到的挑战性问题时常常面临困境。尽管课程强化学习作为一种通过提出一系列替代任务来解决复杂任务的框架展现出合理效果，但以往大多数工作仍因缺乏无需领域先验知识即可获得校准引导至期望结果状态的机制，而在课程生成方面存在困难。为此，我们提出了一种基于不确定性与时间距离感知的课程目标生成方法，通过求解二分匹配问题实现结果导向强化学习。该方法不仅能精确提供校准化的课程引导至期望结果状态，相比以往课程强化学习方法，还具备显著提升的样本效率与几何无关的课程目标提案能力。我们通过定量和定性的方式证明，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，显著优于先前方法。