Learning to select relevant perspective in a dynamic environment

— When an agent observes its environment, there are two important characteristics of the perceived information. One is the relevance of information and the other is redundancy. The irrelevant and redundant features which commonly exists within an environment, commonly leads to agent state explosion and associated high computational cost within the learning process. This paper presents an efficient method concerning both the relevance of information and the correlation in order to improve the learning of reinforcement learning agent. We introduce a new concurrent online learning method to calculate the match count C(s) and relevance degree I(s) to quantify the redundancy and correlation of features with respect to a desired learning task. Our analysis shows that the correlation relationship of the features can be extracted and projected to concurrent biased learning threads. By comparing the commonalities of these learning threads, we can evaluate the relevance degree of a feature tha...

• Type: Conference
• Year: 2008
• Where: IJCNN
• Authors: Zhihui Luo, David A. Bell, Barry McCollum, Qingxiang Wu
• Keywords: Artificial Intelligence, IJCNN 2008, Learning Method, Learning Task, Reinforcement Learning Agent