对偶蒙特卡罗树搜索
Dual Monte Carlo Tree Search
摘要：
AlphaZero使用深度神经网络和蒙特卡罗树搜索（MCTS）的组合，成功地以tabula-rasa的方式训练了强化学习代理。神经MCTS算法已经成功地通过自我游戏找到了接近最优的游戏策略。然而，AlphaZero算法有一个明显的缺点;它需要很长的时间来收敛，并需要高的计算能力，由于复杂的神经网络解决像国际象棋，围棋，将棋等游戏，由于这一点，这是很难追求神经MCTS研究没有尖端的硬件，这是许多有抱负的神经MCTS研究人员的一个障碍。在本文中，我们提出了一种新的神经MCTS算法，称为双MCTS，这有助于克服这些缺点。双MCTS使用两种不同的搜索树，一个深度神经网络，以及一种新的搜索树更新技术，该技术使用PUCB、滑动窗口和搜索-贪心算法的组合。该技术适用于任何基于MCTS的算法，以减少对树的更新次数。我们表明，双MCTS表现优于最广泛使用的神经MCTS算法之一，AlphaZero，各种对称和非对称的游戏。
英文摘要：
AlphaZero, using a combination of Deep Neural Networks and Monte Carlo Tree Search (MCTS), has successfully trained reinforcement learning agents in a tabula-rasa way. The neural MCTS algorithm has been successful in finding near-optimal strategies for games through self-play. However, the AlphaZero algorithm has a significant drawback; it takes a long time to converge and requires high computational power due to complex neural networks for solving games like Chess, Go, Shogi, etc. Owing to this, it is very difficult to pursue neural MCTS research without cutting-edge hardware, which is a roadblock for many aspiring neural MCTS researchers. In this paper, we propose a new neural MCTS algorithm, called Dual MCTS, which helps overcome these drawbacks. Dual MCTS uses two different search trees, a single deep neural network, and a new update technique for the search trees using a combination of the PUCB, a sliding-window, and the epsilon-greedy algorithm. This technique is applicable to any MCTS based algorithm to reduce the number of updates to the tree. We show that Dual MCTS performs better than one of the most widely used neural MCTS algorithms, AlphaZero, for various symmetric and asymmetric games.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
