A Unified Perspective on Value Backup and Exploration in Monte-Carlo Tree Search. (arXiv:2202.07071v1 [cs.AI])

Monte-Carlo Tree Search (MCTS) is a class of methods for solving complex
decision-making problems through the synergy of Monte-Carlo planning and
Reinforcement Learning (RL). The highly combinatorial nature of the problems
commonly addressed by MCTS requires the use of efficient exploration strategies
for navigating the planning tree and quickly convergent value backup methods.
These crucial problems are particularly evident in recent advances that combine
MCTS with deep neural networks for function approximation. In this work, we
propose two methods for …

ai arxiv exploration monte-carlo perspective search tree value