通过任务切换提升学习效率，模拟人类混合学习方式。
Interleaved Multitask Learning with Energy Modulated Learning Progress
- 基于学习进度与神经能耗动态交替任务，实现非顺序学习。
- 平均学习准确率超越随机交替与顺序学习方法。
- 兼顾性能与能耗，适合资源受限的持续学习场景。
人类在学习新技能时会混合应用已有知识并保持过往经验，这启发了机器学习中的持续学习研究。然而，现有方法通常不模拟人类因偏好或环境变化而交替进行任务的学习模式。本文提出一种多任务学习架构，根据任务无关指标（如学习进度和神经计算能耗）动态交替任务。我们在一个模拟机械臂执行一系列效果预测任务的环境中进行了系统性实验。结果表明，该方法在平均学习准确率上优于随机交替和顺序学习策略；同时，引入能耗指标后，虽降低神经能耗，仍能保持良好性能，展现出高效且节能的优势。
原文摘要 · Abstract (English)
As humans learn new skills and apply their existing knowledge while maintaining previously learned information, "continual learning" in machine learning aims to incorporate new data while retaining and utilizing past knowledge. However, existing machine learning methods often does not mimic human learning where tasks are intermixed due to individual preferences and environmental conditions. Humans typically switch between tasks instead of completely mastering one task before proceeding to the next. To explore how human-like task switching can enhance learning efficiency, we propose a multi task learning architecture that alternates tasks based on task-agnostic measures such as "learning progress" and "neural computational energy expenditure". To evaluate the efficacy of our method, we run several systematic experiments by using a set of effect-prediction tasks executed by a simulated manipulator robot. The experiments show that our approach surpasses random interleaved and sequential task learning in terms of average learning accuracy. Moreover, by including energy expenditure in the task switching logic, our approach can still perform favorably while reducing neural energy expenditure.
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