基于强化学习的第一人称射击游戏bot自适应射击

F. Glavin, M. G. Madden
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引用次数: 25

摘要

在当前最先进的商业第一人称射击游戏中,计算机控制的机器人(也称为非玩家角色)通常很容易与人类控制的机器人区分开来。诸如导航失败、人类玩家行踪的“第六感”知识以及确定性、脚本化行为都是导致这种情况的一些原因。然而,我们认为这些游戏中最大的非人类行为指标之一是机器人的武器射击能力。始终如一的精准度和从任何距离“锁定”对手都是机器人的表现能力,这是人类玩家所没有的。传统上,机器人在某种程度上受到定时反应延迟或随机干扰的限制,这不能随着时间的推移而适应或改进其技术。我们假设让bot通过试错来学习射击技能,就像人类玩家学习的方式一样,将导致游戏玩法的更大变化,并产生更不可预测的非玩家角色。本文描述了一种强化学习射击机制,该机制基于对对手造成伤害的动态奖励信号,随着时间的推移适应射击。
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Adaptive Shooting for Bots in First Person Shooter Games Using Reinforcement Learning
In current state-of-the-art commercial first person shooter games, computer controlled bots, also known as nonplayer characters, can often be easily distinguishable from those controlled by humans. Tell-tale signs such as failed navigation, “sixth sense” knowledge of human players' whereabouts and deterministic, scripted behaviors are some of the causes of this. We propose, however, that one of the biggest indicators of nonhumanlike behavior in these games can be found in the weapon shooting capability of the bot. Consistently perfect accuracy and “locking on” to opponents in their visual field from any distance are indicative capabilities of bots that are not found in human players. Traditionally, the bot is handicapped in some way with either a timed reaction delay or a random perturbation to its aim, which doesn't adapt or improve its technique over time. We hypothesize that enabling the bot to learn the skill of shooting through trial and error, in the same way a human player learns, will lead to greater variation in game-play and produce less predictable nonplayer characters. This paper describes a reinforcement learning shooting mechanism for adapting shooting over time based on a dynamic reward signal from the amount of damage caused to opponents.
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来源期刊
IEEE Transactions on Computational Intelligence and AI in Games
IEEE Transactions on Computational Intelligence and AI in Games COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
4.60
自引率
0.00%
发文量
0
审稿时长
>12 weeks
期刊介绍: Cessation. The IEEE Transactions on Computational Intelligence and AI in Games (T-CIAIG) publishes archival journal quality original papers in computational intelligence and related areas in artificial intelligence applied to games, including but not limited to videogames, mathematical games, human–computer interactions in games, and games involving physical objects. Emphasis is placed on the use of these methods to improve performance in and understanding of the dynamics of games, as well as gaining insight into the properties of the methods as applied to games. It also includes using games as a platform for building intelligent embedded agents for the real world. Papers connecting games to all areas of computational intelligence and traditional AI are considered.
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