Customized learning algorithms for episodic tasks with acyclic state spaces

Abstract

The work presented in this paper provides a practical, customized learning algorithm for reinforcement learning tasks that evolve episodically over acyclic state spaces. The presented results are motivated by the Optimal Disassembly Planning (ODP) problem described in [14], and they complement and enhance some earlier developments on this problem that were presented in [15]. In particular, the proposed algorithm is shown to be a substantial improvement of the original algorithm developed in [15], in terms of, both, the involved computational effort and the attained performance, where the latter is measured by the accumulated reward. The new algorithm also leads to a robust performance gain over the typical Q-learning implementations for the considered problem context.