||Author(s): M. Taylor, N. Charboni, A. Fachantidis, I. Vlahavas, L. Torrey.
Title: “Reinforcement Learning Agents Providing Advice in Complex Video Games”.
Click here to download the PDF (Acrobat Reader) file.
Connection Science, Taylor & Francis, (in press), 2014.
Abstract: This paper introduces a teacher-student framework for reinforcement learning. In this frame-
work, a teacher agent instructs a student agent by suggesting actions the student should take
as it learns. However, the teacher may only give such advice a limited number of times. We
present several novel algorithms that teachers can use to budget their advice effectively, and
we evaluate them in two experimental domains: Mountain Car and Pac-Man. Our results
show that the same amount of advice, given at different moments, can have different effects
on student learning, and that teachers can significantly affect student learning even when
students use different learning methods and state representations.