Dimensionality reduction of scene and enemy information in Mario

Mario AI is one of competitions on Computational Intelligence. In the case of video games, agents have to cope with a large number of input information in order to decide their actions at every time step. We have proposed the use of Isomap, a famous Manifold Learning, to reduce the dimensionality of inputs. Especially, we have applied it into scene information. In this paper, we newly extend to enemy information, where the number of enemies is not fixed. Hence, we introduce the proximity metrics in terms of enemies. The generated low-dimensional data is used for input values of Neural Networks. That is, at every time step, transferred data by using a map from raw inputs into the low-dimensional data are presented to Neural Networks. Experimental results on Mario AI environment show the effectiveness of the proposed approach.