Abstract:Abstract. We experiment the introduction of machine learning tools to improve Monte-Carlo Tree Search. More precisely, we propose the use of Direct Policy Search, a classical reinforcement learning paradigm, to learn the Monte-Carlo Move Generator. We experiment our algorithm on different forms of unit commitment problems, including experiments on a problem with both macrolevel and microlevel decisions.
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