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：近期大量机器学习研究论文标题中出现了“开放式学习”一词。然而，这些研究极少尝试明确该术语的具体含义。更甚者，深入分析后发现，学界对于开放式学习与持续学习、终身学习或自生成学习等相关概念的区别并未达成共识。本文致力于改善这一现状。在追溯这一概念的发展脉络及近期对其本质的认知后，我们指出开放式学习通常被视作一个复合概念，涵盖多种不同特性。与既有研究路径不同，我们提出分离出开放式过程的核心基础属性——即在无限时间跨度内持续产生新颖内容的能力。基于此，我们构建了开放式学习问题的概念体系，并重点聚焦于面向目标的开放式强化学习问题子集，因其框架有助于定义渐进式技能库的学习过程。最后，我们指出当前研究仍需填补基础定义与发育人工智能研究者所构想的更复杂开放式学习概念之间的认知鸿沟。