Parameter Decision Making in Adaptive Markov Decision Process with Finite Planning Horizon
Han Zheng-zhi · 2000
An algorithm is proposed for adaptive MDP with finite planning horizon by reason of the fact that all current algorithms only consider adaptive MDP with infinite planning horizon. Bayes principle is applied to learn an unknown system; and for every decision the probability that the actual decision equals the optimal decision is maximized. Simulation results demonstrate the validity of the new algorithm.