Model Reduction Techniques for Computing Approximately Optimal Solutions for Markov Decision Processes

@inproceedings{citeulike:687388, abstract = {We present a method for solving implicit (factored) Markov decision processes (MDPs) with very large state spaces. We introduce a property of state space partitions which we call ffl-homogeneity. Intuitively, an ffl-homogeneous partition groups together states that behave approximately the same under all or some subset of policies. Borrowing from recent work on model minimization in computer-aided software verification, we present an algorithm that takes a factored representation of...}, author = {Dean, Thomas and Givan, Robert and Leach, Sonia }, citeulike-article-id = {687388}, keywords = {abstraction, learning, reinforcement, state}, pages = {124--131}, posted-at = {2006-06-06 18:44:59}, priority = {2}, title = {Model Reduction Techniques for Computing Approximately Optimal Solutions for Markov Decision Processes}, url = {http://citeseer.ist.psu.edu/289035.html} }

TY - CONF ID - citeulike:687388 L3 - citeulike-article-id:687388 TI - Model Reduction Techniques for Computing Approximately Optimal Solutions for Markov Decision Processes SP - 124 EP - 131 N2 - We present a method for solving implicit (factored) Markov decision processes (MDPs) with very large state spaces. We introduce a property of state space partitions which we call ffl-homogeneity. Intuitively, an ffl-homogeneous partition groups together states that behave approximately the same under all or some subset of policies. Borrowing from recent work on model minimization in computer-aided software verification, we present an algorithm that takes a factored representation of... KW - abstraction KW - learning KW - reinforcement KW - state AU - Dean, Thomas AU - Givan, Robert AU - Leach, Sonia UR - http://citeseer.ist.psu.edu/289035.html ER -