Some Insights into Lifelong Reinforcement Learning Systems
arXiv:2001.09608v1
AI Analysis
This addresses the problem of building continuous learning agents for AI, but it is incremental as it offers only insights and a basic prototype.
The paper argues that traditional reinforcement learning fails to model lifelong learning systems and provides insights along with a simplistic prototype.
A lifelong reinforcement learning system is a learning system that has the ability to learn through trail-and-error interaction with the environment over its lifetime. In this paper, I give some arguments to show that the traditional reinforcement learning paradigm fails to model this type of learning system. Some insights into lifelong reinforcement learning are provided, along with a simplistic prototype lifelong reinforcement learning system.