Summary of Efficient Monte Carlo Tree Search Via On-the-fly State-conditioned Action Abstraction, by Yunhyeok Kwak et al.
Efficient Monte Carlo Tree Search via On-the-Fly State-Conditioned Action Abstraction
by Yunhyeok Kwak, Inwoo Hwang, Dooyoung Kim, Sanghack Lee, Byoung-Tak Zhang
First submitted to arxiv on: 2 Jun 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 A novel approach is proposed to enhance Monte Carlo Tree Search (MCTS) in decision-making problems with vast combinatorial action spaces. The method, called state-conditioned action abstraction, learns a latent dynamics model that captures relevant sub-actions based on the current state and compositional structure between states and sub-actions. This allows for more efficient exploration by discarding redundant sub-actions during tree traversal. Compared to vanilla MuZero, the proposed method demonstrates superior sample efficiency in experimental results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MCTS is a powerful tool used to make decisions in many areas. However, it can be slow when there are many different actions to choose from. To solve this problem, researchers have developed a new way to improve MCTS called state-conditioned action abstraction. This method helps MCTS by learning which actions are most important based on the current situation. It does this without needing to know exactly how the environment works. The new approach is tested and shown to be much faster than traditional methods. |
