The complexity of most modem systems prohibits a hand-coded approach to decision making. In addition, many problems have continuous or large discrete state spaces; some have large or continuous action spaces. The problem of learning in large spaces is tackled through generalisation techniques, which allow compact representation of learned information and transfer of knowledge between similar states and actions. In this paper Kanerva. coding and reinforcement learning are combined to produce the KaBaGe-RL decision-making module. The purpose of KaBaGe-RL is twofold. Firstly, Kanerva coding is used as a generalisation method to produce a feature vector from the raw sensory input. Secondly, the reinforcement learning uses this feature vector in order to learn an optimal policy. The efficiency of KaBaGe-RL is tested using the "3 versus 2 possession football" challenge, a subproblem of the RoboCup domain. The results demonstrate that the learning approach outperforms a number of benchmark policies including a hand-coded one.
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