Abstract-Neural network-based controllers are evolved for racing simulated RIC cars around several tracks of varying difficulty. The transferability of driving skills acquired when evolving for a single track is evaluated, and different ways of evolving controllers able to perform well on many different tracks are investigated. It is further shown that such generally proficient controllers can reliably be developed into specialized controllers for individual tracks. Evolution of sensor parameters together with network weights is shown to lead to higher final fitness, but only if turned on after a general controller is developed, otherwise it hinders evolution. It is argued that simulated car racing is a scalable and relevant testbed for evolutionary robotics research, and that the results of this research can be useful for commercial computer games. Keywords: Evolutionary robotics, games, car racing, driving, incremental evolution Car racing is a remarkably popular preoccupation -both to watch and to participate in -be it in a computer simulation or in the "real world". But it is not only popular, it is also challenging: racing well requires fast and accurate reactions, knowledge of the car's behaviour in different environments, and various forms of real-time planning, such as path planning and deciding when to overtake a competitor. In other words, it requires many of the core components of intelligence being researched within computational intelligence and robotics. The success of the recent DARPA Grand Challenge [l], where completely autonomous real cars raced in a demanding desert environment, may be taken as a measure of the interest in car racing within these research communities. This paper deals with using evolutionary algorithms to create neural network controllers for simulated car racing. Specifically, we evolve controllers that have robust performance over different tracks, and can be specialized to work better on particular tracks. Evolutionary robotics (the use of evolutionary algorithms for embodied control problems) and simulated car racing are in many ways ideal companions. The benefit for the development of racing games and simulations is clear: evolutionary robotics offers a way to automatically develop controllers, possibly specialized for specific tracks or types of tracks, driving styles, skill levels, competitors etc. One could envision a racing simulator where the user is allowed to construct his own tracks and cars, and the game automatically develops a set of controllers to drive these tracks.
Then a neural-network based controller REF , that was trained on human driver behaviour, was testing if a generated track is challenging enough for a particular driver.
Evolving robust and specialized car racing skills
2006 IEEE International Conference on Evolutionary Computation
2006 IEEE International Conference on Evolutionary Computation
Computer Science