论文标题
使用强化学习解决皇家游戏
Solving Royal Game of Ur Using Reinforcement Learning
论文作者
论文摘要
强化学习最近已成为解决棋盘游戏领域中复杂问题的非常有力的工具，其中通常需要代理商根据自己的经验和收到的奖励来学习复杂的策略和移动。尽管RL优于用于玩简单的视频游戏和受欢迎的棋盘游戏的现有最新方法，但它尚未证明其在古代游戏中的能力。在这里，我们解决了一个这样的问题，在那里我们使用不同的方法来训练代理商，即蒙特卡洛，Qlearning和希望SARSA能够学习最佳政策，以发挥战略性的皇家乌尔游戏。我们游戏的状态空间很复杂，但是我们的代理商在玩游戏和学习重要的战略动作方面表现出令人鼓舞的结果。尽管很难得出结论，当接受有限的资源培训时，算法总体上的表现会更好，但预计SARSA在学习方面表现出了令人鼓舞的结果。
Reinforcement Learning has recently surfaced as a very powerful tool to solve complex problems in the domain of board games, wherein an agent is generally required to learn complex strategies and moves based on its own experiences and rewards received. While RL has outperformed existing state-of-the-art methods used for playing simple video games and popular board games, it is yet to demonstrate its capability on ancient games. Here, we solve one such problem, where we train our agents using different methods namely Monte Carlo, Qlearning and Expected Sarsa to learn optimal policy to play the strategic Royal Game of Ur. The state space for our game is complex and large, but our agents show promising results at playing the game and learning important strategic moves. Although it is hard to conclude that when trained with limited resources which algorithm performs better overall, but Expected Sarsa shows promising results when it comes to fastest learning.
