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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 <jats:inline-formula><jats:alternatives><jats:tex-math>$$L^*$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msup>\n                    <mml:mi>L<\/mml:mi>\n                    <mml:mo>\u2217<\/mml:mo>\n                  <\/mml:msup>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>-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\u2019s performance. We compare our approach to an existing approach that synthesizes an automaton directly from the POMDP, thereby solving it. 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