AI

Abstract

QWOP is a popular Flash game in which a human player controls a sprinter in a simulated 100-meter dash. The game is notoriously difficult owing to its ragdoll physics engine, and the simultaneous movements that must be carefully coordinated to achieve forward progress. While previous researchers have evolved gaits using simulations similar to QWOP, we describe a software interface that connects directly to QWOP itself, incorporating a genetic algorithm to evolve actual QWOP gaits. Since QWOP has no API, ours detects graphical screen elements and uses them to build a fitness function. Two variable-length encoding schemes, that codify sequences of QWOP control commands that loop to form gaits, are tested. We then compare the performance of SGA, Genitor, and a Cellular Genetic Algorithm on this task. Using only the end score as the basis for fitness, the cellular algorithm is consistently able to evolve a successful scooting strategy similar to one most humans employ. The results confirm that steady-state GAs are preferred when the task is sensitive to small input variations. Although the limited feedback does not yet produce performance competitive with QWOP champions, it is the first autonomous software evolution of successful QWOP gaits.