  Strategies for partially observable Markov decision processes (POMDP)
typically require memory. One way to represent this memory is via automata. We
present a method to learn an automaton representation of a strategy using a
modification of the L*-algorithm. Compared to the tabular representation of a
strategy, the resulting automaton is dramatically smaller and thus also more
explainable. Moreover, in the learning process, our heuristics may even improve
the strategy's performance. In contrast to approaches that synthesize an
automaton directly from the POMDP thereby solving it, our approach is
incomparably more scalable.
