  In this article we study the problem of training intelligent agents using
Reinforcement Learning for the purpose of game development. Unlike systems
built to replace human players and to achieve super-human performance, our
agents aim to produce meaningful interactions with the player, and at the same
time demonstrate behavioral traits as desired by game designers. We show how to
combine distinct behavioral policies to obtain a meaningful "fusion" policy
which comprises all these behaviors. To this end, we propose four different
policy fusion methods for combining pre-trained policies. We further
demonstrate how these methods can be used in combination with Inverse
Reinforcement Learning in order to create intelligent agents with specific
behavioral styles as chosen by game designers, without having to define many
and possibly poorly-designed reward functions. Experiments on two different
environments indicate that entropy-weighted policy fusion significantly
outperforms all others. We provide several practical examples and use-cases for
how these methods are indeed useful for video game production and designers.
