论文标题
将进化搜索与行为克隆相结合的程序生成内容
Combining Evolutionary Search with Behaviour Cloning for Procedurally Generated Content
论文作者
论文摘要
在这项工作中，我们考虑了视频游戏水平的程序内容生成问题。先前的方法取决于能够生成不同级别的进化搜索方法，但是这一代过程很慢，这在实时设置中是有问题的。还提出了加强学习（RL）来解决同样的问题，尽管水平生成很快，但训练时间可能非常昂贵。我们提出了一个框架，以解决结合ES和RL最好的程序内容生成问题。特别是，我们的方法首先使用ES来生成一系列级别，然后使用行为克隆将这些级别的级别分配到策略中，然后可以查询该级别以快速产生新的水平。我们将方法应用于迷宫游戏和Super Mario Bros，结果表明我们的方法实际上减少了水平生成所需的时间，尤其是在需要越来越多的有效水平时。
In this work, we consider the problem of procedural content generation for video game levels. Prior approaches have relied on evolutionary search (ES) methods capable of generating diverse levels, but this generation procedure is slow, which is problematic in real-time settings. Reinforcement learning (RL) has also been proposed to tackle the same problem, and while level generation is fast, training time can be prohibitively expensive. We propose a framework to tackle the procedural content generation problem that combines the best of ES and RL. In particular, our approach first uses ES to generate a sequence of levels evolved over time, and then uses behaviour cloning to distil these levels into a policy, which can then be queried to produce new levels quickly. We apply our approach to a maze game and Super Mario Bros, with our results indicating that our approach does in fact decrease the time required for level generation, especially when an increasing number of valid levels are required.
