首页> 外文会议>Innovative Computing, Information and Control (ICICIC-2009), 2009 >Applying Reinforcement Learning for Game AI in a Tank-Battle Game
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Applying Reinforcement Learning for Game AI in a Tank-Battle Game

机译:在坦克战中将强化学习应用于游戏AI

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Reinforcement learning is an unsupervised machine learning method in the area of artificial intelligence. It presents well performance in simulation of the thinking ability of human. However, it needs a trial-and-error process to achieve the goal. In the research field of game AI, it is a good approach to allow the non-player-characters (NPCs) of digital games to become more humanity. In this paper, we try to build a tank-battle computer game and use the methodology of reinforcement learning for the NPCs (tanks). The goal of this paper is to make this game become more interesting from the enhanced interactions with these intelligent NPCs.
机译:强化学习是人工智能领域中一种无监督的机器学习方法。它在模拟人的思维能力方面表现出色。但是,它需要一个反复试验的过程才能实现目标。在游戏AI的研究领域中,这是一种使数字游戏的非玩家角色(NPC)变得更加人性化的好方法。在本文中,我们尝试构建一个坦克战计算机游戏,并为NPC(坦​​克)使用强化学习的方法。本文的目的是通过与这些智能NPC的增强互动使该游戏变得更加有趣。

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