Simulating trajectories of virtual crowds is a commonly encountered task in Computer
Graphics. Several recent works have applied Reinforcement Learning methods to animate
virtual agents, however they often make different design choices when it comes to the
fundamental simulation setup. Each of these choices comes with a reasonable justification
for its use, so it is not obvious what is their real impact, and how they affect the results. In this
work, we analyze some of these arbitrary choices in terms of their impact on the learning�…