Efficient Q-Learning by Division of Labor

Q-learning as well as other learning paradigms depend strongly on the representation of the underlying state space. As a special case of the hidden state problem we investigate the effect of a self-organizing discretization of the state space in a simple control problem. We apply the neural gas algorithm with adaptation of learning rate and neighborhood range to a simulated cart-pole problem. The learning parameters are determined by the ambiguity of successful actions inside each cell