The field of Continual Learning (CL) seeks to develop algorithms that accumulate knowledge and skills over time through interaction with non-stationary environments. In practice, a plethora of evaluation procedures (settings) and algorithmic solutions (methods) exist, each with their own potentially disjoint set of assumptions. This variety makes measuring progress in CL difficult. We propose a taxonomy of settings, where each setting is described as a set of assumptions. A tree-shaped hierarchy emerges from this view, where more general settings become the parents of those with more restrictive assumptions. This makes it possible to use inheritance to share and reuse research, as developing a method for a given setting also makes it directly applicable onto any of its children. We instantiate this idea as a publicly available software framework called Sequoia, which features a wide variety of settings from both the Continual Supervised Learning (CSL) and Continual Reinforcement Learning (CRL) domains. Sequoia also includes a growing suite of methods which are easy to extend and customize, in addition to more specialized methods from external libraries. We hope that this new paradigm and its first implementation can help unify and accelerate research in CL. You can help us grow the tree by visiting www.github.com/lebrice/Sequoia.

翻译：持续学习（Continual Learning, CL）领域旨在开发能够通过与动态环境交互随时间积累知识和技能的算法。实践中存在大量的评估流程（场景）和算法解决方案（方法），各自具有可能互不兼容的假设集。这种多样性使得衡量CL领域的进展变得困难。我们提出一种场景分类法，将每个场景描述为一组假设。由此形成树状层次结构，其中更通用的场景成为具有更严格限制假设场景的父节点。这使得通过继承机制共享和复用研究成果成为可能——为特定场景开发的方法可直接适用于其所有子场景。我们将此理念实现为名为Sequoia的开源软件框架，该框架涵盖持续监督学习（CSL）和持续强化学习（CRL）两个领域中的多种场景。除集成外部库中更专门化的方法外，Sequoia还包含一套易于扩展和定制的持续增长方法库。我们期待这一新范式及其首个实现能帮助统一并加速CL领域的研究。欢迎访问www.github.com/lebrice/Sequoia共同培育这棵技术之树。