Evolving Neuromodulatory Topologies for Plasticity in Video Game Playing

Sammanfattning: In the last decades neural networks have become more frequent in video games. Neuroevolution helps us generate optimal network topologies for specific tasks, but there are still still unexplored areas of neuroevolution, and ways of improving the performance of neural networks, which we could utilize for video game playing. The aim of this thesis is to find a suitable fitness evaluation and improve the plasticity of evolved neural networks, as well as comparing the performance and general video game playing abilities of established neuroevolution methods. Using Analog Genetic Encoding we implement evolving neuromodulatory topologies in a typical two-dimensional platformer video game, and have it play against itself without neuromodulation, and against a popular genetic algorithm known as Neuroevolution of Augmenting Topologies. A suitable input and output structure is developed as well as an appropriate fitness evaluation for properly mating and mutating a population of neural networks. The benefits of neuromodulation are tested by running and completing a number of tile-based platformer video game levels. The results show an increased performance in networks influenced by neuromodulators, but no general video game playing abilities are obtained. This shows us that a more advanced general gameplay learning method with higher influence is required.