Humans engage in learning and reviewing processes with curricula when acquiring new skills or knowledge. This human learning behavior has inspired the integration of curricula with replay methods in continual learning agents. The goal is to emulate the human learning process, thereby improving knowledge retention and facilitating learning transfer. Existing replay methods in continual learning agents involve the random selection and ordering of data from previous tasks, which has shown to be effective. However, limited research has explored the integration of different curricula with replay methods to enhance continual learning. Our study takes initial steps in examining the impact of integrating curricula with replay methods on continual learning in three specific aspects: the interleaved frequency of replayed exemplars with training data, the sequence in which exemplars are replayed, and the strategy for selecting exemplars into the replay buffer. These aspects of curricula design align with cognitive psychology principles and leverage the benefits of interleaved practice during replays, easy-to-hard rehearsal, and exemplar selection strategy involving exemplars from a uniform distribution of difficulties. Based on our results, these three curricula effectively mitigated catastrophic forgetting and enhanced positive knowledge transfer, demonstrating the potential of curricula in advancing continual learning methodologies. Our code and data are available: https://github.com/ZhangLab-DeepNeuroCogLab/Integrating-Curricula-with-Replays

翻译：人类在获取新技能或知识时，会通过课程计划进行学习与复习。这种人类学习行为启发我们在持续学习智能体中整合课程学习与重放方法，旨在模拟人类学习过程，从而提升知识保留并促进学习迁移。现有持续学习智能体中的重放方法通常涉及从以往任务中随机选择数据和排序，已被证明有效。然而，关于如何通过结合不同课程与重放方法来增强持续学习的研究仍十分有限。本研究初步探讨了课程与重放方法整合对持续学习的影响，具体涉及三个方面：重放示例与训练数据的交错频率、示例重放的顺序，以及将示例选入重放缓冲区的策略。这些课程设计维度与认知心理学原理相契合，能充分利用重放过程中的交错练习、从易到难的复习策略，以及基于均匀难度分布的示例选择策略。实验结果表明，这三种课程有效缓解了灾难性遗忘，并增强了正向知识迁移，展现了课程在推动持续学习方法发展中的潜力。我们的代码与数据已公开：https://github.com/ZhangLab-DeepNeuroCogLab/Integrating-Curricula-with-Replays