A lot of recent machine learning research papers have "Open-ended learning" in their title. But very few of them attempt to define what they mean when using the term. Even worse, when looking more closely there seems to be no consensus on what distinguishes open-ended learning from related concepts such as continual learning, lifelong learning or autotelic learning. In this paper, we contribute to fixing this situation. After illustrating the genealogy of the concept and more recent perspectives about what it truly means, we outline that open-ended learning is generally conceived as a composite notion encompassing a set of diverse properties. In contrast with these previous approaches, we propose to isolate a key elementary property of open-ended processes, which is to always produce novel elements from time to time over an infinite horizon. From there, we build the notion of open-ended learning problems and focus in particular on the subset of open-ended goal-conditioned reinforcement learning problems, as this framework facilitates the definition of learning a growing repertoire of skills. Finally, we highlight the work that remains to be performed to fill the gap between our elementary definition and the more involved notions of open-ended learning that developmental AI researchers may have in mind.

翻译：近年来，许多机器学习研究论文在标题中使用了“开放结局学习”一词，但鲜有尝试界定其具体含义。更甚者，深入审视后发现，学界对于开放结局学习与持续学习、终身学习或自生成学习等相关概念的区别缺乏共识。本文旨在改善这一现状。通过阐述该概念的发展脉络及关于其真正含义的最新观点，我们指出开放结局学习通常被视为一个包含多种不同属性的复合概念。与这些先前方法不同，我们提出隔离开放结局过程的一个关键基本属性：即在无限时间范围内不断生成新元素。基于此，我们构建了开放结局学习问题的概念，并特别聚焦于开放结局目标条件强化学习问题的子类，因为该框架有助于定义学习不断增长技能库的过程。最后，我们强调了为弥合基本定义与发展人工智能研究者心中更复杂的开放结局学习概念之间差距所需开展的工作。