The Cross Entropy Method for Fast Policy Search

Shie Mannor, Reuven Rubinstein, and Yohai Gat

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.

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