Using reinforcement learning for city site selection in the turn-based strategy game Civilization IV
Using reinforcement learning for city site selection in the turn-based strategy game Civilization IV
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在回合制策略游戏《文明 IV》中使用强化学习进行城市选址
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
I. Watson
中科院分区：
文献类型：
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作者：
S. Wender;I. Watson
本文描述了基于Q-Learning的强化学习器的设计和实现。该自适应代理应用于商业电脑游戏《文明IV》中的城市布局选择任务。城市布局选择决定了这款文明系列回合制帝国建设游戏中城市的创建地点。我们的目标是为最初由复杂的确定性脚本执行的任务创建一种自适应机器学习方法。这种机器学习方法带来了更具挑战性和动态的计算机人工智能。我们展示了强化学习方法性能的初步结果，并对自适应代理和原始静态游戏人工智能的性能进行了比较。比较和性能测量都显示出令人鼓舞的结果。此外，还详细阐述了学习算法的行为和性能，并讨论了扩展我们工作的方法。
This paper describes the design and implementation of a reinforcement learner based on Q-Learning. This adaptive agent is applied to the city placement selection task in the commercial computer game Civilization IV. The city placement selection determines the founding sites for the cities in this turn-based empire building game from the Civilization series. Our aim is the creation of an adaptive machine learning approach for a task which is originally performed by a complex deterministic script. This machine learning approach results in a more challenging and dynamic computer AI. We present the preliminary findings on the performance of our reinforcement learning approach and we make a comparison between the performance of the adaptive agent and the original static game AI. Both the comparison and the performance measurements show encouraging results. Furthermore the behaviour and performance of the learning algorithm are elaborated and ways of extending our work are discussed.