课程与重放的整合：其对持续学习的影响
Integrating Curricula with Replays: Its Effects on Continual Learning
AI总结：
本研究受人类课程式学习行为启发，探索课程与重放方法整合对持续学习的影响，从交错频率、重放顺序、样例选择策略三方面设计课程，有效缓解灾难性遗忘并增强正向知识迁移。
摘要：
人类在学习新技能或新知识时，会按照课程开展学习与复习流程。这种人类学习行为启发了持续学习智能体中将课程与重放方法的整合，其目标是模拟人类学习过程，从而提升知识留存能力并促进学习迁移。现有持续学习智能体中的重放方法会从先前任务中随机选择并排序数据，已被证明是有效的。然而，鲜有研究探索将不同课程与重放方法相结合以增强持续学习的效果。本研究初步考察了课程与重放方法的整合对持续学习的影响，具体聚焦三个方面：重放样例与训练数据的交错频率、样例的重放顺序、将样例选入重放缓冲区的策略。这些课程设计方面符合认知心理学原理，利用了重放期间交错练习、从易到难复习，以及涉及难度均匀分布样例的样例选择策略带来的益处。根据我们的结果，这三种课程有效缓解了灾难性遗忘，增强了正向知识迁移，证明了课程在推进持续学习方法方面的潜力。我们的代码和数据可在以下链接获取：https://github.com/ZhangLab-DeepNeuroCogLab/Integrating-Curricula-with-Replays
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
