Radial Basis Function Networks for Autonomous Agent Control

While many learning algorithms as well as dynamic growing and pruning techniques are appropriate for most technical applications, they do not work appropriately in the context of autonomous agents. Autonomous agents potentially operate in dynamically changing environments. They receive an endless data stream, which makes it impossible to store a fixed set of training patterns. Therefore, autonomous agents require network models that, among other properties, feature incremental learning. This paper shows how radial basis function networks can be modified to fit these requirements. Since we are currently developing an appropriate value system for autonomous agents, this paper illustrates the network’s properties on several regression tasks and the well-know double-spiral problem. It is shown that (1) the network yields fast convergence, (2) the presentation of patterns from one subspace does not affect the mapping of other patterns, (3) and the model yields very fast classification; the network learns the double-spiral task within only one epoch.