论文标题
使用特征性的终身学习：任务分离，技能获取和选择性转移
Lifelong Learning using Eigentasks: Task Separation, Skill Acquisition, and Selective Transfer
论文作者
论文摘要
我们介绍了终身学习的特征施加框架。特征性是解决一组相关任务的技能配对，并与可以从技能输入空间中采样的生成模型配对。该框架扩展了生成的重播方法，这些方法主要用于避免灾难性的遗忘，还可以解决其他终身学习目标，例如前瞻性知识转移。我们提出了一个交替的任务学习和知识合并以在我们的框架中学习的唤醒循环，并将其实例化以终身监督学习和终身RL。在监督持续学习中，我们在最新的持续学习方面取得了提高的性能，并在Game Starcraft 2中的终生RL应用中展示了远期知识转移的证据。
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.
