  Nondeterministic Good-for-MDP (GFM) automata are for MDP model checking and
reinforcement learning what good-for-games automata are for reactive synthesis:
a more compact alternative to deterministic automata that displays
nondeterminism, but only so much that it can be resolved locally, such that a
syntactic product can be analysed. GFM has recently been introduced as a
property for reinforcement learning, where the simpler B\"uchi acceptance
conditions it allows to use is key. However, while there are classic and novel
techniques to obtain automata that are GFM, there has not been a decision
procedure for checking whether or not an automaton is GFM. We show that
GFM-ness is decidable and provide an EXPTIME decision procedure as well as a
PSPACE-hardness proof.
