Abstract
Employing a learning system in which the "actor-critic' reinforcement learning method and a Gauss-sigmoid neural network are combined, a high performance learning system can be built due to the combination of the autonomy for reinforcement learning and the generalization capability for neural networks. Much time is needed when the conventional reinforcement learning method is used in order to train a robot with huge number of states such as our 1-legged hopping robot with 3-DOF. In this study, based on a simulation study for a wheel type inverted pendulum, we examined a few learning techniques different from conventional methods from the viewpoint of the improvement of learning speed. The obtained result shows that the increase of the number of modification steps as for connection strengths in neural networks is very effective for the decrease of learning cost.
