# Monte Carlo Search Algorithms Discovering Monte Carlo Tree Search Exploration Terms

arXiv: https://arxiv.org/abs/2404.09304 · HTML: https://mlsanity.com/paper/2404.09304/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 45 references that are in the map.
- First posted on arXiv 14 Apr 2024.

## Builds on (most cited first, 10 of 10)

- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://mlsanity.com/paper/1712.01815/) (2017)
- [Symbolic Discovery of Optimization Algorithms](https://mlsanity.com/paper/2302.06675/) (2023)
- [Monte Carlo Tree Search: A Review of Recent Modifications and Applications](https://mlsanity.com/paper/2103.04931/) (2021)
- [Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree search](https://mlsanity.com/paper/2205.13134/) (2022)
- [Accelerating Self-Play Learning in Go](https://mlsanity.com/paper/1902.10565/) (2019)
- [Quantum Circuit Optimization with AlphaTensor](https://mlsanity.com/paper/2402.14396/) (2024)
- [Automated Quantum Circuit Design with Nested Monte Carlo Tree Search](https://mlsanity.com/paper/2207.00132/) (2022)
- [Discovering Mathematical Formulas from Data via GPT-guided Monte Carlo Tree Search](https://mlsanity.com/paper/2401.14424/) (2024)
- [Vision Transformers for Computer Go](https://mlsanity.com/paper/2309.12675/) (2023)

## Abstract (arXiv)

Monte Carlo Tree Search and Monte Carlo Search have good results for many combinatorial problems. In this paper we propose to use Monte Carlo Search to design mathematical expressions that are used as exploration terms for Monte Carlo Tree Search algorithms. The optimized Monte Carlo Tree Search algorithms are PUCT and SHUSS. We automatically design the PUCT and the SHUSS root exploration terms. For small search budgets of 32 evaluations the discovered root exploration terms make both algorithms competitive with usual PUCT.