  Markov Population Models are a widespread formalism used to model the
dynamics of complex systems, with applications in Systems Biology and many
other fields. The associated Markov stochastic process in continuous time is
often analyzed by simulation, which can be costly for large or stiff systems,
particularly when a massive number of simulations has to be performed (e.g. in
a multi-scale model). A strategy to reduce computational load is to abstract
the population model, replacing it with a simpler stochastic model, faster to
simulate. Here we pursue this idea, building on previous works and constructing
a generator capable of producing stochastic trajectories in continuous space
and discrete time. This generator is learned automatically from simulations of
the original model in a Generative Adversarial setting. Compared to previous
works, which rely on deep neural networks and Dirichlet processes, we explore
the use of state of the art generative models, which are flexible enough to
learn a full trajectory rather than a single transition kernel.
