Advertisement
Published in:
2009 | OriginalPaper | Chapter
Incorporating Expert Advice into Reinforcement Learning Using Constructive Neural Networks
Authors : Robert Ollington, Peter Vamplew, John Swanson
Published in: Constructive Neural Networks
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
This paper presents and investigates a novel approach to using expert advice to speed up the learning performance of an agent operating within a reinforcement learning framework. This is accomplished through the use of a constructive neural network based on radial basis functions. It is demonstrated that incorporating advice from a human teacher can substantially improve the performance of a reinforcement learning agent, and that the constructive algorithm proposed is particularly effective at aiding the early performance of the agent, whilst reducing the amount of feedback required from the teacher. The use of constructive networks within a reinforcement learning context is a relatively new area of research in itself, and so this paper also provides a review of the previous work in this area, as a guide for future researchers.