Summary of Monte Carlo Search Algorithms Discovering Monte Carlo Tree Search Exploration Terms, by Tristan Cazenave
Monte Carlo Search Algorithms Discovering Monte Carlo Tree Search Exploration Terms
by Tristan Cazenave
First submitted to arxiv on: 14 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 proposes using Monte Carlo Search to design mathematical expressions as exploration terms for Monte Carlo Tree Search (MCTS) algorithms, specifically PUCT and SHUSS. The optimized MCTS algorithms leverage the designed expressions to improve performance. By automatically designing the root exploration terms for PUCT and SHUSS, the authors demonstrate competitive results with traditional PUCT methods using small search budgets of 32 evaluations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research uses a new way to make computers find solutions to complex problems faster. They combine two existing techniques, Monte Carlo Search and Tree Search, to design special formulas that help the computer look in the right places for answers. The paper shows that these improved algorithms can solve problems almost as well as more complicated methods using only a small number of “guesses”. |
