Monte Carlo Search Algorithms Discovering Monte Carlo Tree Search Exploration Terms
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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.
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References · 10
- 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,353
- 2017Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithmcited by 734
- 2023Symbolic Discovery of Optimization Algorithmscited by 434
- 2021Monte Carlo Tree Search: A Review of Recent Modifications and Applicationscited by 161
- 2022Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree searchcited by 66
- 2019Accelerating Self-Play Learning in Gocited by 64
- 2024Quantum Circuit Optimization with AlphaTensorcited by 36
- 2022Automated Quantum Circuit Design with Nested Monte Carlo Tree Searchcited by 16
- 2024Discovering Mathematical Formulas from Data via GPT-guided Monte Carlo Tree Searchcited by 10
- 2023Vision Transformers for Computer Gocited by 2
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