The ability for an agent to continuously learn new skills without
catastrophically forgetting existing knowledge is of critical importance for
the development of generally intelligent agents. Most methods devised to
address this problem depend heavily on well-defined task boundaries, and thus
depend on human supervision. Our task-agnostic method, Self-Activating Neural
Ensembles (SANE), uses a modular architecture designed to avoid catastrophic
forgetting without making any such assumptions. At the beginning of each
trajectory, a module in the SANE ensemble is activated to determine the agent's
next policy. During training, new modules are created as needed and only
activated modules are updated to ensure that unused modules remain unchanged.
This system enables our method to retain and leverage old skills, while growing
and learning new ones. We demonstrate our approach on visually rich
procedurally generated environments.

使用自激活神经模块及模块化结构，本文提出了一种基于视觉强化学习环境的无先验任务边界设定的持续学习方法，以避免忘却与利用旧有的技能，并在生成环境中进行了相关实验。