W. B. Knox, P. Stone, C. Breazeal

Abstract

Incorporating human interaction into agent learning yields two crucial benefits. First, human knowledge can greatly improve the speed and final result of learning compared to pure trial-and-error approaches like reinforcement learning. And second, human users are empowered to designate “correct” behavior. In this abstract, we present research on a system for learning from human interaction—the TAMER framework— then point to extensions to TAMER, and finally describe a demonstration of these systems