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**)
New parameters are introduced which can be changed by **change _parametername_**
- _parametername_ can be:
+**trainingruns**: amount of trainingruns
+**discretization**: discretization of the continuous variables for the scheduler, 10 truncates after the first decimal place, 100 after the second etc.
+**prophetic**: true when the scheduler has information on the future general transition firings
+**discount factor**: discount factor parameter required for training
+**alpha**: alpha parameter required for training
+**epsilon**: epsilon parameter for epsilon-greedy exploration
- Mandatory option: **--n** _newvalue_: Number or decimal/boolean with the new parameter value.
