A new technique for reinforcement learning for control

In this work reinforcement learning was successfully applied to several simulated control problems including pendulum swing-up, pole balancing and a difficult challenge that can roughly be described as balancing a basketball on one´s finger. Compared to the first two tasks, the ball-balancing task is much harder. Control is achieved by using a piecewise linear approximant of the Q-function learned by an adaptive logic network (ALN) which solves Bellman´s equation in a high-dimensional space