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Efficient learning algorithms for episodic tasks with acyclic state spaces

This paper considers the problem of computing an optimal policy for a Markov decision process (MDP), under lack of complete a priori knowledge of (i) the branching probability distributions determining the evolution of the process state upon the execution of the different actions, and (ii) the probability distributions characterizing the immediate rewards returned by the environment as a result of the execution of these actions at different states of the process. In addition, it is assumed that the underlying task evolves in a repetitive, episodic manner, with each episode starting from a well-defined initial state and evolving over an acyclic state space. A novel efficient algorithm for this problem is proposed, and its convergence properties and computational complexity are rigorously characterized in the formal framework of computational learning theory