An approach for finding optimal schedulers via reinforcement learning in nondeterministic Hybrid Petri nets with general transitions is implemented on the schedulingML branch and for models including non-linear ODES on dynamicapprox.
Model checking properties can be maximized and minimized (usage similar to **check** but with keyword **maximize**/**minimize**)
