A learning system composed of linear control modules, reinforce(cid:173) ment learning modules and selection modules (a hybrid reinforce(cid:173) ment learning system) is proposed for the fast learning of real-world control problems. The selection modules choose one appropriate control module dependent on the state. This hybrid learning sys(cid:173) tem was applied to the control of a stilt-type biped robot. It learned the control on a sloped floor more quickly than the usual reinforce(cid:173) ment learning because it did not need to learn the control on a flat floor, where the linear control module can control the robot. When it was trained by a 2-step learning (during the first learning step, the selection module was trained by a training procedure con(cid:173) trolled only by the linear controller), it learned the control more quickly.

Papers published at the Neural Information Processing Systems Conference.