Integration and Evaluation of Exploration-Based Learning in Games

Video and computer games provide a rich platform for testing adaptive decision systems such as value-based reinforcement learning and neuroevolution. However, integrating such systems into the game environment and evaluating their performance in it is time and labor intensive. In this paper, an approach is developed for using general integration and evaluation software to alleviate these problems. In particular, the testbed for integrating and evaluating learning techniques (TIELT) is used to integrate a neuroevolution learner with an off-the-shelf computer game Unreal Tournament
^TM5
(Aha and Molineaux, 2004). The resulting system is successfully used to evolve artificial neural network controllers with basic navigation behavior. Our work leads to formulating a set of requirements that make a general integration and evaluation system such as TIELT a useful tool for benchmarking adaptive decision systems