游戏自动化

Shaik Mahammad Aasheesh, K. N. S. R. Reddy, Manideep Yellani, D. N, Soumya Shridhar Hegde
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引用次数: 0

摘要

强化学习在各种游戏中表现非常出色,有可能超越人类水平的游戏玩法。尽管它在回合制游戏中表现不错,但像战斗或更复杂的3D射击游戏等复杂游戏类型仍然是个挑战。创建的ML模型可以自学如何玩特定的游戏,这样它就可以用来测试游戏的完整性、漏洞和不规则性。该模型还用于发现是否存在通过利用某些游戏内部机制来加速游戏运行的方法,或检查是否有任何角色或能力被过度控制。我们为一些游戏创建了模型,以确定这些AI模型的执行情况,并查看在游戏之间切换需要什么样的差异。
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Gameplay Automation
Reinforcement Learning has performed remarkably well in various games and has the potential to surpass human level gameplay. Although it does good in turn-based games, complex game genres like fighting or much more complex 3D shooters are still a challenge. The created ML model can teach itself how to play a specific game so that it can be used to test the games for completeness, bugs, and irregularities. The model is also used to find out if there are any ways to speed run games by exploiting certain in-game mechanisms or to check if any characters or abilities are overpowered. Models were created for a few games to identify how well these AI models can perform and see what kind of differences were required to switch between the games.
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