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Lifelong Learning using Eigentasks: Task Separation, Skill Acquisition, and Selective Transfer
arXiv - CS - Artificial Intelligence Pub Date : 2020-07-14 , DOI: arxiv-2007.06918
Aswin Raghavan, Jesse Hostetler, Indranil Sur, Abrar Rahman, Ajay Divakaran

We introduce the eigentask framework for lifelong learning. An eigentask is a pairing of a skill that solves a set of related tasks, paired with a generative model that can sample from the skill's input space. The framework extends generative replay approaches, which have mainly been used to avoid catastrophic forgetting, to also address other lifelong learning goals such as forward knowledge transfer. We propose a wake-sleep cycle of alternating task learning and knowledge consolidation for learning in our framework, and instantiate it for lifelong supervised learning and lifelong RL. We achieve improved performance over the state-of-the-art in supervised continual learning, and show evidence of forward knowledge transfer in a lifelong RL application in the game Starcraft2.

中文翻译:

使用特征任务的终身学习:任务分离、技能习得和选择性转移

我们介绍了终身学习的特征任务框架。特征任务是解决一组相关任务的技能的配对,与可以从技能的输入空间中采样的生成模型配对。该框架扩展了生成重放方法,主要用于避免灾难性遗忘,还可以解决其他终身学习目标,例如前向知识转移。我们为我们的框架中的学习提出了交替任务学习和知识巩固的唤醒-睡眠循环,并将其实例化以用于终身监督学习和终身强化学习。我们在监督持续学习方面取得了比最先进技术更高的性能,并在星际争霸 2 游戏的终生强化学习应用中展示了前向知识转移的证据。
更新日期:2020-07-15
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