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首页> 外文期刊>Computational Intelligence and AI in Games, IEEE Transactions on >Adaptive Shooting for Bots in First Person Shooter Games Using Reinforcement Learning
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Adaptive Shooting for Bots in First Person Shooter Games Using Reinforcement Learning

机译：使用强化学习对第一人称射击游戏中的机器人进行自适应射击

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摘要

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.
机译：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）通常可以轻松地与人类控制的机器人区分开。诸如失败的导航，关于人类玩家的下落的“第六感”知识以及确定性的脚本化行为等讲故事的迹象是造成这种情况的一些原因。但是，我们建议，可以从机器人的武器射击能力中找到这些游戏中非人性行为的最大指标之一。始终如一的完美准确性以及可以从任何距离“锁定”对手的视野中的对手，这是人类玩家无法发现的机器人的指示能力。传统上，该机器人在某种程度上受到时间延迟的反应延迟或对其目标的随机干扰，从而无法适应或改进其技术。我们假设，使机器人能够像人类玩家一样学习通过反复试验来射击的技巧，将导致游戏玩法变化更大，并产生较少可预测的非玩家角色。本文介绍了一种强化学习射击机制，该机制可根据来自对对手造成的伤害量中的动态奖励信号，随着时间调整射击。

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