论文标题
PCGRL：通过增强学习生成程序内容
PCGRL: Procedural Content Generation via Reinforcement Learning
论文作者
论文摘要
我们研究了如何使用加强学习来训练水平设计的代理。这代表了一种在游戏中的过程中生成的新方法，其中级别的设计被构成游戏，并且学习了内容生成器本身。通过将设计问题视为一项顺序任务，我们可以使用强化学习来学习如何采取下一个动作，以最大程度地提高预期的最终水平质量。当有几个或没有示例可以训练时，可以使用这种方法，而训练有素的发电机非常快。我们研究了将二维设计问题转换为马尔可夫决策过程的三种不同方法，并将其应用于三个游戏环境。
We investigate how reinforcement learning can be used to train level-designing agents. This represents a new approach to procedural content generation in games, where level design is framed as a game, and the content generator itself is learned. By seeing the design problem as a sequential task, we can use reinforcement learning to learn how to take the next action so that the expected final level quality is maximized. This approach can be used when few or no examples exist to train from, and the trained generator is very fast. We investigate three different ways of transforming two-dimensional level design problems into Markov decision processes and apply these to three game environments.
