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Monte-Carlo tree search as regularized policy optimization

Abstract : The combination of Monte-Carlo tree search (MCTS) with deep reinforcement learning has led to significant advances in artificial intelligence. However, AlphaZero, the current state-of-the-art MCTS algorithm, still relies on hand-crafted heuristics that are only partially understood. In this paper, we show that AlphaZero's search heuristics, along with other common ones such as UCT, are an approximation to the solution of a specific regularized policy optimization problem. With this insight, we propose a variant of AlphaZero which uses the exact solution to this policy optimization problem, and show experimentally that it reliably outperforms the original algorithm in multiple domains.
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Submitted on : Sunday, September 27, 2020 - 12:12:09 PM
Last modification on : Friday, December 18, 2020 - 6:46:06 PM
Long-term archiving on: : Thursday, December 3, 2020 - 6:36:54 PM


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  • HAL Id : hal-02950136, version 1


Jean-Bastien Grill, Florent Altché, Yunhao Tang, Thomas Hubert, Michal Valko, et al.. Monte-Carlo tree search as regularized policy optimization. International Conference on Machine Learning, 2020, Vienna, Austria. ⟨hal-02950136⟩



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