# An Optimal Computing Budget Allocation Tree Policy for Monte Carlo Tree Search

arXiv: https://arxiv.org/abs/2009.12407 · HTML: https://mlsanity.com/paper/2009.12407/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2020.

## Built on by (most cited first, 2 of 2)

- [Convergence Rate Analysis for Optimal Computing Budget Allocation Algorithms](https://mlsanity.com/paper/2211.14722/) (2022)
- [An Efficient Dynamic Sampling Policy For Monte Carlo Tree Search](https://mlsanity.com/paper/2204.12043/) (2022)

## Abstract (arXiv)

We analyze a tree search problem with an underlying Markov decision process, in which the goal is to identify the best action at the root that achieves the highest cumulative reward. We present a new tree policy that optimally allocates a limited computing budget to maximize a lower bound on the probability of correctly selecting the best action at each node. Compared to widely used Upper Confidence Bound (UCB) tree policies, the new tree policy presents a more balanced approach to manage the exploration and exploitation trade-off when the sampling budget is limited. Furthermore, UCB assumes that the support of reward distribution is known, whereas our algorithm relaxes this assumption. Numerical experiments demonstrate the efficiency of our algorithm in selecting the best action at the root.