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Adjutant bot: An evaluation of unit micromanagement tactics

机译:辅助机器人:对单元微管理策略的评估

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Constructing an effective real-time strategy bot requires multiple interlocking elements including a well-designed architecture, efficient build order, and good strategic and tactical decision-making. However even when the bot's high-level strategy and resource allocation is sound, poor battlefield tactics can result in unnecessary losses. This paper focuses on the problem of avoiding troop loss by identifying good tactical groupings. Banding separated units together using UCT (Upper Confidence bounds applied to Trees) along with a learned reward model outperforms grouping heuristics at winning battles while preserving resources. This paper describes our findings in the context of the Adjutant bot design which won the best Newcomer honor at CIG 2012 and is the basis for our 2013 entry.
机译:构建一个有效的实时战略机器人需要多个相互关联的要素,包括精心设计的架构,有效的构建顺序以及良好的战略和战术决策。但是,即使机器人的高级策略和资源分配合理,劣质的战场策略也可能导致不必要的损失。本文着重于通过确定良好的战术分组来避免部队损失的问题。使用UCT(应用于树的最高可信度边界)将分离的单位结合在一起,并结合学习的奖励模型,在赢得战斗时胜过将启发式算法分组,同时保留资源。本文在Adjutant机器人设计的背景下描述了我们的发现,该设计在CIG 2012上获得了Newcomer的最佳荣誉,并且是我们2013年参赛作品的基础。

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