In this paper we present a layered approach for multi-agent control problem,
decomposed into three stages, each building upon the results of the previous
one. First, a high-level plan for a coarse abstraction of the system is
computed, relying on parametric timed automata augmented with stopwatches as
they allow to efficiently model simplified dynamics of such systems. In the
second stage, the high-level plan, based on SMT-formulation, mainly handles the
combinatorial aspects of the problem, provides a more dynamically accurate
solution. These stages are collectively referred to as the SWA-SMT solver. They
are correct by construction but lack a crucial feature: they cannot be executed
in real time. To overcome this, we use SWA-SMT solutions as the initial
training dataset for our last stage, which aims at obtaining a neural network
control policy. We use reinforcement learning to train the policy, and show
that the initial dataset is crucial for the overall success of the method