Modern video games are becoming richer and more complex in terms of game mechanics. This complexity allows for the emergence of a wide variety of ways to play the game across the players. From the point of view of the game designer, this means that one needs to anticipate a lot of different ways the game could be played. Machine Learning (ML) could help address this issue. More precisely, Reinforcement Learning is a promising answer to the need of automating video game testing. In this paper we present a video game environment which lets us define multiple play-styles. We then introduce CARI: a Configurable Agent with Reward as Input. An agent able to simulate a wide continuum range of play-styles. It is not constrained to extreme archetypal behaviors like current methods using reward shaping. In addition it achieves this through a single training loop, instead of the usual one loop per play-style. We compare this novel training approach with the more classic reward shaping approach and conclude that CARI can also outperform the baseline on archetypes generation. This novel agent could be used to investigate behaviors and balancing during the production of a video game with a realistic amount of training time.
翻译:现代电子游戏在游戏机学方面变得越来越丰富和复杂。 这种复杂性使得游戏机师可以出现各种各样的游戏方式。 从游戏设计师的角度来看, 这意味着人们需要预见到游戏可以玩耍的多种不同方式。 机器学习( ML) 可以帮助解决这一问题。 更准确地说, 强化学习是满足自动游戏游戏测试需要的一个很有希望的答案。 在本文中, 我们展示了一个可以让我们定义多种游戏风格的视频游戏环境。 我们随后引入了 CARI: 一个具有 Reward 的可配置代理物。 一个能够模拟一系列广泛的游戏机型连续操作的代理物。 它可以不局限于像目前使用奖赏塑造的方法那样的极端的拱形行为。 此外, 它可以通过一个单一的培训循环来达到这一点, 而不是通常的每个游戏式的循环。 我们比较这个新颖的培训方法与更经典的奖赏塑造方法, 并得出结论, CARI 也可以超越成型一代的基线。 这个新型代理商可以用来调查行为, 并用现实的培训时间来平衡制作一个视频游戏时段。