AlphaZero is a self-play reinforcement learning algorithm that achieves superhuman play in chess, shogi, and Go via policy iteration. To be an effective policy improvement operator, AlphaZero's search requires accurate value estimates for the states appearing in its search tree. AlphaZero trains upon self-play matches beginning from the initial state of a game and only samples actions over the first few moves, limiting its exploration of states deeper in the game tree. We introduce Go-Exploit, a novel search control strategy for AlphaZero. Go-Exploit samples the start state of its self-play trajectories from an archive of states of interest. Beginning self-play trajectories from varied starting states enables Go-Exploit to more effectively explore the game tree and to learn a value function that generalizes better. Producing shorter self-play trajectories allows Go-Exploit to train upon more independent value targets, improving value training. Finally, the exploration inherent in Go-Exploit reduces its need for exploratory actions, enabling it to train under more exploitative policies. In the games of Connect Four and 9x9 Go, we show that Go-Exploit learns with a greater sample efficiency than standard AlphaZero, resulting in stronger performance against reference opponents and in head-to-head play. We also compare Go-Exploit to KataGo, a more sample efficient reimplementation of AlphaZero, and demonstrate that Go-Exploit has a more effective search control strategy. Furthermore, Go-Exploit's sample efficiency improves when KataGo's other innovations are incorporated.

翻译：AlphaZero是一个自我游戏强化学习算法,在象棋、shogi和政策迭代中实现超人游戏。为了成为一个有效的政策改进操作员,阿尔法Zero的搜索要求对其搜索树中出现的国家进行准确的值估。阿尔法Zero从游戏的初始状态开始进行自玩比赛的列车,仅对刚开始的几步进行抽样行动,限制对游戏树更深的状态的探索。我们引入了Go-Exploit,这是阿尔法Zero的新型搜索控制策略。Go-Exploit从一个利益国的档案中抽取其自玩轨道的起始状态。开始自玩轨道需要从不同的起始状态进行精确的定位。开始自玩游戏开始的自动轨迹,需要从不同的起始状态开始对状态进行精确的轨迹估计,让Go-Exploit更加有效地探索树进行探索, 从而可以更高效地进行更精确的运行。 在Verequen Go Go 上,我们比Vereal Go-dedeal Go-de 学习了另一个更精确的动作,我们学习了比Veal-Geal-Go-Go-Go-Go-</s>