学习在第一人称射击游戏中拦截对手

Bulent Tastan, Yuan Chang, G. Sukthankar
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引用次数: 25

摘要

创造游戏bot的一个重要方面是对抗行动计划:确定如何移动以对抗对手可能采取的行动。在本文中,我们研究了对手拦截问题,其中机器人的目标是可靠地捕获对手。我们提出了一种运动规划算法,该算法将规划和预测结合起来,在部分遮挡的虚幻锦标赛地图上拦截敌人。人类玩家在移动偏好上可以表现出相当大的可变性,并不总是喜欢相同的路线。为了模拟这种可变性,我们使用逆强化学习从一组示例轨迹中学习特定于玩家的运动模型。对手的运动预测使用粒子滤波来跟踪多个时间范围内对手位置的候选假设。研究结果表明,与其他运动模型和预测方法相比,该学习运动模型具有更高的跟踪精度和更好的拦截效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Learning to intercept opponents in first person shooter games
One important aspect of creating game bots is adversarial motion planning: identifying how to move to counter possible actions made by the adversary. In this paper, we examine the problem of opponent interception, in which the goal of the bot is to reliably apprehend the opponent. We present an algorithm for motion planning that couples planning and prediction to intercept an enemy on a partially-occluded Unreal Tournament map. Human players can exhibit considerable variability in their movement preferences and do not uniformly prefer the same routes. To model this variability, we use inverse reinforcement learning to learn a player-specific motion model from sets of example traces. Opponent motion prediction is performed using a particle filter to track candidate hypotheses of the opponent's location over multiple time horizons. Our results indicate that the learned motion model has a higher tracking accuracy and yields better interception outcomes than other motion models and prediction methods.
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