An object-oriented approach to reinforcement learning in an action game

Abstract

In this work, we look at the challenge of learning in an action game, Infinite Mario. Learning to play an action game can be divided into two distinct but related problems, learning an object-related behavior and selecting a primitive action.We propose a framework that allows for the use of reinforcement learning for both of these problems. We present promising results in some instances of the game and identify some problems that might affect learning. Copyright © 2011, Association for the Advancement of Artificial.