Summary of Monte Carlo Tree Search with Boltzmann Exploration, by Michael Painter et al.
Monte Carlo Tree Search with Boltzmann Exploration
by Michael Painter, Mohamed Baioumy, Nick Hawes, Bruno Lacerda
First submitted to arxiv on: 11 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 focuses on improving Monte-Carlo Tree Search (MCTS) methods for automated planning techniques. Specifically, it addresses limitations in Maximum ENtropy Tree-Search (MENTS), a method that uses Boltzmann policies to encourage exploration. The authors propose two new algorithms, Boltzmann Tree Search (BTS) and Decaying ENtropy Tree-Search (DENTS), which address these limitations while preserving the benefits of Boltzmann policies. The paper evaluates the performance of these algorithms on several benchmark domains, including Go. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at planning and decision-making. Right now, there are some computer programs that can play games like Go really well, but they’re not perfect. This research tries to fix a problem with one of those programs called Maximum ENtropy Tree-Search (MENTS). The authors come up with two new ways for the program to make decisions that work better and are more efficient. They test these methods on different types of games and show that they do well. |
