Exploring evolutionary learning in a simulated hockey environment

As a test bed for studying evolutionary and other machine learning techniques, we have developed a simulated hockey game called Shock in which players attempt to shoot a puck into their enemy´s goal during a fixed time period. Multiple players may participate-one can be controlled by a human user, while the others are guided by artificial controllers. In previous work, we introduced the Shock environment and presented players that received global input (as if from an overhead camera) and were trained on a restricted task, using an evolutionary hill climbing algorithm, with a staged learning approach (A. Blair and E. Sklar, 1998). Here, we expand upon this work by developing players which instead receive input from local, Braitenberg-style sensors (V. Braitenberg, 1984). These players are able to learn the task with fewer restrictions, using a simpler fitness measure based purely on whether or not a goal was scored. Moreover, they evolve to develop robust strategies for moving around the rink and scoring goals