Learning Behavior from Limited Demonstrations in the Context of Games

A significant amount of work has advocated that Learning from Demonstration (LfD) is a promising approach to allow end-users to create behaviors for in-game characters without requiring programming. However, one major problem with this approach is that many LfD algorithms require large amounts of training data, and thus are not practical. In this paper, we focus on LfD with limited training data, and specifically on the problem of Active Learning from Demonstration in settings where the amount of data that can be queried from the demonstrator is limited by a predefined budget. We extend our novel Active Learning from Demonstration approach, SALT, and compare it to related LfD algorithms in both task performance (reward) and similarity to the demonstrator's behavior, when used with relatively small amounts of training data. We use Super Mario Bros. and two variations of the Thermometers puzzle game as our evaluation domains.