Authors: H. Handa
Addresses: Okayama University, Tsushima-Naka 3-1-1, Okayama, Japan
Abstract: Evolutionary learning of neural networks, i.e., neuroevolution, has shown to play an important role in agent constitutions. It has the robustness property for dynamic, practical problems. In the case of a large number of input neurons, however, the search space of neuroevolution becomes much larger so that it is difficult to find out better policies. In this paper, Isomap, one of the manifold learning algorithms, is employed to reduce the dimensionality of the input space. The Isomap tries to reduce the dimensionality based on manifold structures in high dimensional space and to preserve local topological relationships among data. Mario AI is used as a test bed for the proposed method. Video games such as Mario, Ms. Pac-Man, and car racing have been recognised as ideal benchmark problems for computational intelligence, where they require a variety of inputs, real-time strategy, and so on, and they provide good simulators which are capable to apply CI techniques. A large number of scenes in Mario are applied by the Isomap in order to constitute a map from scene information to low dimensional data. Experimental results on the Mario AI show the effectiveness of the proposed method.
Keywords: neuroevolution; manifold learning; particle swarm optimisation; PSO; Mario; Isomap; evolutionary learning; neural networks; video games; computational intelligence.
International Journal of Bio-Inspired Computation, 2012 Vol.4 No.1, pp.14 - 26
Available online: 16 Jan 2012 *Full-text access for editors Access for subscribers Purchase this article Comment on this article