  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 performance, as well as the quality of the resulting simulation
measured in terms of the energy efficiency. We perform a theoretical analysis
of the properties of the reward function design, and empirically evaluate the
impact of using certain observation and action spaces on a variety of
scenarios, with the reward function and energy usage as metrics. We show that
directly using the neighboring agents' information as observation generally
outperforms the more widely used raycasting. Similarly, using nonholonomic
controls with egocentric observations tends to produce more efficient behaviors
than holonomic controls with absolute observations. Each of these choices has a
significant, and potentially nontrivial impact on the results, and so
researchers should be mindful about choosing and reporting them in their work.
