论文标题
开发成功的轰炸机代理商
Developing a Successful Bomberman Agent
论文作者
论文摘要
在本文中，我们研究了AI的方法，以成功地扮演2-4个玩家，完整的信息，Bomberman变体在Codingame Platform上发布。我们比较了三种搜索算法的行为：蒙特卡洛树搜索，滚动地平线进化和光束搜索。我们提出了各种增强功能，从而提高了代理商的力量，即涉及搜索，对手预测，游戏状态评估和游戏引擎编码。我们的顶级代理变体基于具有基于低级位的状态表示和评估功能的光束搜索，该功能依赖于基于基于模拟的生存估计来修剪未经促进状态的态度。它在编码名称竞技场提交的2300名AI代理中达到了最高的位置。
In this paper, we study AI approaches to successfully play a 2-4 players, full information, Bomberman variant published on the CodinGame platform. We compare the behavior of three search algorithms: Monte Carlo Tree Search, Rolling Horizon Evolution, and Beam Search. We present various enhancements leading to improve the agents' strength that concern search, opponent prediction, game state evaluation, and game engine encoding. Our top agent variant is based on a Beam Search with low-level bit-based state representation and evaluation function heavy relying on pruning unpromising states based on simulation-based estimation of survival. It reached the top one position among the 2,300 AI agents submitted on the CodinGame arena.
