  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 present a video game
environment which lets us define multiple play-styles. We then introduce CARI:
a Configurable Agent with Reward as Input. An agent able to simulate a wide
continuum range of play-styles. It is not constrained to extreme archetypal
behaviors like current methods using reward shaping. In addition it achieves
this through a single training loop, instead of the usual one loop per
play-style. We compare this novel training approach with the more classic
reward shaping approach and conclude that CARI can also outperform the baseline
on archetypes generation. This novel agent could be used to investigate
behaviors and balancing during the production of a video game with a realistic
amount of training time.

Artificial Intelligence;Machine Learning