TY - JOUR
T1 - Increasing generality in machine learning through procedural content generation
AU - Risi, Sebastian
AU - Togelius, Julian
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Computational science
KW - Computer science
KW - Computational science
KW - Computer science
U2 - 10.1038/s42256-020-0208-z
DO - 10.1038/s42256-020-0208-z
M3 - Journal article
VL - 2
SP - 428
EP - 436
JO - Nature Machine Intelligence
JF - Nature Machine Intelligence
IS - 8
ER -