This paper considers the eeect of input-space partitioning on reinforcement learning for control. In many such learning systems, the input space is partitioned by the system designer. However, input-space partitioning could be learned. Our objective is to compare learned and programmed input-space partitionings in terms of the overall system learning speed and proociency achieved. We present a ...

ion and relational learning Charles Kemp & Alan Jern Department of Psychology Carnegie Mellon University {ckemp,ajern}@cmu.edu Abstract Most models of categorization learn categories defined by characteristic features but some categories are described more naturally in terms of relations. We present a generative model that helps to explain how relational categories are learned and used. Our mod...