On study of temporal difference method in decision process and its application

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In decision process with uncertainty, the optimal solutions are constructed by using dynamic programming(DP) algorithms. In order to solve practical problems involving very large state space, we need to decrease the amount of computation necessary for learning algorithm since DP algorithm cannot be applied directly. Based on using the data of state-action process, the value function is estimated by learning algorithm. We consider temporal difference method of Neuro Dynamic Programming (Neuro-DP) and Bayesian interval estimation in Markov decision processes with unknown transition law. We derive algorithms of constructing optimal solution theoretically. We also treat numerical examples to show the validity of algorithms and improve corresponding algorithms.