Yu-Hung Chang

Regret-Guided Search Control for Efficient Learning in AlphaZero

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 occu…