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 handcrafted 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.
Keywords
Cite
@article{arxiv.2007.12509,
title = {Monte-Carlo Tree Search as Regularized Policy Optimization},
author = {Jean-Bastien Grill and Florent Altché and Yunhao Tang and Thomas Hubert and Michal Valko and Ioannis Antonoglou and Rémi Munos},
journal= {arXiv preprint arXiv:2007.12509},
year = {2020}
}
Comments
Accepted to International Conference on Machine Learning (ICML), 2020