Procedural content generation (PCG) refers to the practice of generating game content, such as levels, quests or characters, algorithmically. Motivated by the need to make games replayable, as well as to reduce authoring burden and enable particular aesthetics, many PCG methods have been devised. At the same time that researchers are adapting methods from machine learning (ML) to PCG problems, the ML community has become more interested in PCG-inspired methods. One reason for this development is that ML algorithms often only work for a particular version of a particular task with particular initial parameters. In response, researchers have begun exploring randomization of problem parameters to counteract such overfitting and to allow trained policies to more easily transfer from one environment to another, such as from a simulated robot to a robot in the real world. Here we review existing work on PCG, its overlap with current efforts in ML, and promising new research directions such as procedurally generated learning environments. Although originating in games, we believe PCG algorithms are critical to creating more general machine intelligence.

程序内容生成（PCG）是指通过算法生成游戏内容（例如关卡，任务或角色）的实践。出于使游戏可重玩以及减轻创作负担和实现特殊美感的需求的动机，已经设计了许多PCG方法。在研究人员使方法从机器学习（ML）适应PCG问题的同时，ML社区对PCG启发的方法也越来越感兴趣。这种发展的一个原因是ML算法通常仅适用于具有特定初始参数的特定任务的特定版本。作为回应，研究人员已开始探索问题参数的随机化措施，以抵消这种过拟合问题，并使经过培训的政策更容易从一种环境转移到另一种环境，例如从模拟机器人到现实世界中的机器人。在这里，我们回顾了PCG的现有工作，其与ML当前工作的重叠以及有希望的新研究方向，例如程序生成的学习环境。尽管起源于游戏，但我们认为PCG算法对于创建更通用的机器智能至关重要。