  This work discusses the benefits of having multiple simulated environments
with different degrees of realism for the development of algorithms in
scenarios populated by autonomous nodes capable of communication and mobility.
This approach aids the development experience and generates robust algorithms.
It also proposes GrADyS-SIM NextGen as a solution that enables development on a
single programming language and toolset over multiple environments with varying
levels of realism. Finally, we illustrate the usefulness of this approach with
a toy problem that makes use of the simulation framework, taking advantage of
the proposed environments to iteratively develop a robust solution.
