PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Q-Learning and Enhanced Policy Iteration in Discounted Dynamic Programming
Dimitri Bertsekas and Huizhen Yu
In: The 49th IEEE Conference on Decision and Control (CDC)(2010).


We consider the classical finite-state discounted Markovian decision problem, and we introduce a new policy iteration-like algorithm for finding the optimal Q-factors. Instead of policy evaluation by solving a linear system of equations, our algorithm involves (possibly inexact) solution of an optimal stopping problem. This problem can be solved with simple Q-learning iterations, in the case where a lookup table representation is used; it can also be solved with the Q-learning algorithm of Tsitsiklis and Van Roy [TsV99], in the case where feature-based Q-factor approximations are used. In exact/lookup table representation form, our algorithm admits asynchronous and stochastic iterative implementations, in the spirit of asynchronous/modified policy iteration, with lower overhead advantages over existing Q-learning schemes. Furthermore, for large-scale problems, where linear basis function approximations and simulation-based temporal difference implementations are used, our algorithm resolves effectively the inherent difficulties of existing schemes due to inadequate exploration.

EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:8067
Deposited By:Huizhen Yu
Deposited On:17 March 2011