تقرير
Monte Carlo Tree Search with Boltzmann Exploration
| العنوان: | Monte Carlo Tree Search with Boltzmann Exploration |
|---|---|
| المؤلفون: | Painter, Michael, Baioumy, Mohamed, Hawes, Nick, Lacerda, Bruno |
| المصدر: | Advances in Neural Information Processing Systems 36 (2024) |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Artificial Intelligence, Computer Science - Machine Learning |
| الوصف: | Monte-Carlo Tree Search (MCTS) methods, such as Upper Confidence Bound applied to Trees (UCT), are instrumental to automated planning techniques. However, UCT can be slow to explore an optimal action when it initially appears inferior to other actions. Maximum ENtropy Tree-Search (MENTS) incorporates the maximum entropy principle into an MCTS approach, utilising Boltzmann policies to sample actions, naturally encouraging more exploration. In this paper, we highlight a major limitation of MENTS: optimal actions for the maximum entropy objective do not necessarily correspond to optimal actions for the original objective. We introduce two algorithms, Boltzmann Tree Search (BTS) and Decaying ENtropy Tree-Search (DENTS), that address these limitations and preserve the benefits of Boltzmann policies, such as allowing actions to be sampled faster by using the Alias method. Our empirical analysis shows that our algorithms show consistent high performance across several benchmark domains, including the game of Go. Comment: Camera ready version of NeurIPS2023 paper |
| نوع الوثيقة: | Working Paper |
| الوصول الحر: | http://arxiv.org/abs/2404.07732Test |
| رقم الانضمام: | edsarx.2404.07732 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |