Fuzzy decision tree function approximation in reinforcement learning

Recent results on reinforcement learning regarding the convergence of control algorithms with function approximators, have shown that decision tree based reinforcement learning provides good learning performance and more reliable convergence than the neural network approach. It scales better to larger input spaces with lower memory requirements, and can solve problems that are infeasible using table lookup. However, decision tree based reinforcement learning can deal with only discrete actions. In realistic applications, it is imperative to deal with continuous states and actions. In this paper, we have proposed fuzzy decision tree based reinforcement learning that takes care of the limitations of decision tree based learning. We compare our approach with decision tree based function approximator on two bench mark problems: inverted pendulum stabilisation problem and two-link robot manipulator tracking problem.