论文标题
通过增强学习来创建较弱的抽象棋盘游戏代理的技术
A Technique to Create Weaker Abstract Board Game Agents via Reinforcement Learning
论文作者
论文摘要
除了独奏游戏外，棋盘游戏至少需要其他玩家才能玩。因此，当对手失踪时，我们创建了人工智能（AI）代理商来对抗我们。这些AI代理是通过多种方式创建的，但是这些代理的一个挑战是，与我们相比，代理可以具有较高的能力。在这项工作中，我们描述了如何创建玩棋盘游戏的较弱的AI代理。我们使用Tic-Tac-toe，九名成员的Morris和Mancala，我们的技术使用了强化学习模型，代理商使用Q学习算法来学习这些游戏。我们展示了这些代理商如何学会完美地玩棋盘游戏，然后我们描述了制作这些代理商较弱版本的方法。最后，我们提供了比较AI代理的方法。
Board games, with the exception of solo games, need at least one other player to play. Because of this, we created Artificial Intelligent (AI) agents to play against us when an opponent is missing. These AI agents are created in a number of ways, but one challenge with these agents is that an agent can have superior ability compared to us. In this work, we describe how to create weaker AI agents that play board games. We use Tic-Tac-Toe, Nine-Men's Morris, and Mancala, and our technique uses a Reinforcement Learning model where an agent uses the Q-learning algorithm to learn these games. We show how these agents can learn to play the board game perfectly, and we then describe our approach to making weaker versions of these agents. Finally, we provide a methodology to compare AI agents.
