  Automata learning techniques automatically generate system models from test
observations. These techniques usually fall into two categories: passive and
active. Passive learning uses a predetermined data set, e.g., system logs. In
contrast, active learning actively queries the system under learning, which is
considered more efficient.
  An influential active learning technique is Angluin's L* algorithm for
regular languages which inspired several generalisations from DFAs to other
automata-based modelling formalisms. In this work, we study L*-based learning
of deterministic Markov decision processes, first assuming an ideal setting
with perfect information. Then, we relax this assumption and present a novel
learning algorithm that collects information by sampling system traces via
testing. Experiments with the implementation of our sampling-based algorithm
suggest that it achieves better accuracy than state-of-the-art passive learning
techniques with the same amount of test data. Unlike existing learning
algorithms with predefined states, our algorithm learns the complete model
structure including the states.
