Creating large numbers of game AIs by learning behavior for cooperating units

机译：通过学习合作单位的行为来创建大量的游戏AI

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We present two improvements to the hybrid learning method for the shout-ahead architecture for units in the game Battle for Wesnoth. The shout-ahead architecture allows for units to perform decision making in two stages, first determining an action without knowledge of the intentions of other units, then, after communicating the intended action and likewise receiving the intentions of the other units, taking these intentions into account for the final decision on the next action. The decision making uses two rule sets and reinforcement learning is used to learn rule weights (that influence decision making), while evolutionary learning is used to evolve good rule sets. Our improvements add knowledge about terrain to the learning and also evaluate unit behaviors on several scenario maps to learn more general rules. The use of terrain knowledge resulted in improvements in the win percentage of evolved teams between 3 and 14 percentage points for different maps, while using several maps to learn from resulted in nearly similar win percentages on maps not learned from as on the maps learned from.

机译：我们为韦诺之战游戏中的单位提供了针对喊叫式架构的混合学习方法的两项改进。预先喊叫架构允许单位分两个阶段执行决策，首先是在不了解其他单位意图的情况下确定一项行动，然后在传达了预期的行动并同样接收其他单位的意图之后，将这些意图纳入对下一个动作的最终决定负责。决策制定使用两个规则集，强化学习用于学习规则权重（影响决策制定），而进化学习则用于发展良好的规则集。我们的改进为学习增加了有关地形的知识，还评估了几种方案图上的单位行为，以了解更多通用规则。地形知识的使用使不同地图的成长战队的胜率提高了3％至14个百分点，而使用几张地图来学习却无法从获悉的地图上获得几乎相似的获胜百分比。

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《2013 IEEE Conference on Computatonal Intelligence in Games》|2013年|1-8|共8页
会议地点 Niagara Falls(CA)
作者
Wiens Stephen; Denzinger Jorg; Paskaradevan Sanjeev;
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Department of Computer Science University of Calgary Calgary, AB, Canada, T2N IN4c;

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正文语种 eng
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4. Creating large numbers of game AIs by learning behavior for cooperating units [C] . Wiens Stephen, Denzinger Jorg, Paskaradevan Sanjeev IEEE Conference on Computatonal Intelligence in Games . 2013

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Creating large numbers of game AIs by learning behavior for cooperating units

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