Summary of Anytime Sequential Halving in Monte-carlo Tree Search, by Dominic Sagers and Mark H.m. Winands and Dennis J.n.j. Soemers
Anytime Sequential Halving in Monte-Carlo Tree Search
by Dominic Sagers, Mark H.M. Winands, Dennis J.N.J. Soemers
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents an anytime version of Monte-Carlo Tree Search (MCTS) for minimizing simple regret in the root node, building upon previous work that utilized Sequential Halving. The proposed algorithm approximates the behavior of Sequential Halving while avoiding its requirement for a predetermined budget. Empirical evaluations on synthetic multi-armed bandit (MAB) problems and ten board games demonstrate competitive performance with UCB1 and Sequential Halving-based MCTS methods. The anytime algorithm’s ability to be halted at any arbitrary time and still produce satisfactory results makes it an attractive solution for real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper improves a computer game-playing method called Monte-Carlo Tree Search (MCTS). MCTS helps computers make decisions by weighing the pros and cons of different options. In this case, the goal is to make better choices at the very beginning of the game. The new algorithm can stop whenever you want it to, and it’s still good at making decisions. The researchers tested their method on lots of games and found that it performs well compared to other methods. This could be useful in real-life situations where computers need to make quick decisions. |
