Configurable Agent With Reward As Input: A Play-Style Continuum Generation

@article{Woillemont2021ConfigurableAW,
  title={Configurable Agent With Reward As Input: A Play-Style Continuum Generation},
  author={Pierre Le Pelletier de Woillemont and R{\'e}mi Labory and Vincent Corruble},
  journal={2021 IEEE Conference on Games (CoG)},
  year={2021},
  pages={1-8}
}
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… 
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