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[2602.20809] Regret-Guided Search Control for Efficient Learning in AlphaZero
Summary
This article presents Regret-Guided Search Control (RGSC), a novel approach to enhance the learning efficiency of AlphaZero by prioritizing high-regret states during training.
Why It Matters
The development of RGSC is significant as it addresses the inefficiencies in reinforcement learning, particularly in how agents learn from past experiences. By focusing on high-regret states, this method could lead to faster learning and improved performance in AI systems, making it relevant for advancements in machine learning and AI applications.
Key Takeaways
- RGSC improves AlphaZero's learning efficiency by focusing on high-regret states.
- The method outperforms previous approaches, achieving significant Elo rating gains.
- Utilizing a prioritized regret buffer allows for better reuse of valuable training states.
- RGSC enhances robustness and efficiency in training AI agents.
- The approach could influence future AI training methodologies.
Computer Science > Machine Learning arXiv:2602.20809 (cs) [Submitted on 24 Feb 2026] Title:Regret-Guided Search Control for Efficient Learning in AlphaZero Authors:Yun-Jui Tsai, Wei-Yu Chen, Yan-Ru Ju, Yu-Hung Chang, Ti-Rong Wu View a PDF of the paper titled Regret-Guided Search Control for Efficient Learning in AlphaZero, by Yun-Jui Tsai and 4 other authors View PDF HTML (experimental) Abstract:Reinforcement learning (RL) agents achieve remarkable performance but remain far less learning-efficient than humans. While RL agents require extensive self-play games to extract useful signals, humans often need only a few games, improving rapidly by repeatedly revisiting states where mistakes occurred. This idea, known as search control, aims to restart from valuable states rather than always from the initial state. In AlphaZero, prior work Go-Exploit applies this idea by sampling past states from self-play or search trees, but it treats all states equally, regardless of their learning potential. We propose Regret-Guided Search Control (RGSC), which extends AlphaZero with a regret network that learns to identify high-regret states, where the agent's evaluation diverges most from the actual outcome. These states are collected from both self-play trajectories and MCTS nodes, stored in a prioritized regret buffer, and reused as new starting positions. Across 9x9 Go, 10x10 Othello, and 11x11 Hex, RGSC outperforms AlphaZero and Go-Exploit by an average of 77 and 89 Elo, respectively. ...
