SOURCE-LINKED INTELLIGENCE
Homeostatic Continual Learning
In this paper, I formulate a Continual Learning problem and propose a method named "Homeostatic Continual Learning" that enables an AI agent to learn continuously in a changing environment without catastrophic forgetting. The core of the method is to find outliers in the environment data when the agent experiences an outlier in its output. Through this method, the agent gradually completes its model and policy and performs well in more and more contexts. I also suggest that we may use the method to build a world model where the agent factorizes the objects in the world into features, abstract
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-12T07:29:29.000Z
First collected: 2026-09-20T16:41:15.630Z. This is not the publication date.