Multigrid Methods for Policy Evaluation and Reinforcement Learning
Omer Ziv, Nahum Shimkin · 2005
We introduce a new class of multigrid temporal-difference learning algorithms for speeding up the estimation of the value function related to a stationary policy, within the context of discounted cost Markov decision processes with linear functional approximation. The proposed scheme builds on the multi-grid framework which is used in numerical analysis to enhance the iterative solution of linear equations. We first apply the multigrid approach to policy evaluation in the known model case. We then extend this approach to the learning case, and propose a scheme in which the basic TD(lambda) learning algorithm is applied at various resolution scales. The efficacy of the proposed algorithms is demonstrated through simulation experiments