The Cross Entropy Method for Fast Policy Search

Proceedings of the Twentieth International Conference on Machine Learning

Volume

Proceedings of the Twentieth International Conference on Machine Learning

Contents

We present a learning framework for Markovian decision processes that is based on optimization in the policy space. Instead of using relatively slow gradient-based optimization algorithms, we use the fast Cross Entropy method. The suggested framework is described for several reward criteria and its effectiveness is demonstrated for a grid world navigation task and for an inventory control problem.