Partitioning input space for reinforcement learning for control

This paper considers the effect 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 fixed input-space partitionings in terms of the overall system learning speed and proficiency achieved. We present a system for unsupervised control-learning in temporal domains with results for both fixed and learned input-space partitionings. The trailer-backing task is used as an example problem