A RELATIVE VALUE ITERATION Q-LEARNING ALGORITHM AND ITS CONVERGENCE BASED-ON FINITE SAMPLES

A reinforcement learning agent solves its decision problems by learning optimal decision mapping from a state to an action. There are two approaches generally to solving optimal decision, the one for maximum reward, the other for optimal cost. This paper is concerned with the problem of a novel Q-learning algorithm for solving optimal cost function. Q-learning is a reinforcement learning method to solve Markovian decision problems with incomplete information. In this paper, beginning with solving optimality cost function, the relative value iteration Q-learning algorithm is proposed. It can make many results of the dynamics programming algorithm available for studying Q-learning directly.