  We study the problem of identifying the policy space of a learning agent,
having access to a set of demonstrations generated by its optimal policy. We
introduce an approach based on statistical testing to identify the set of
policy parameters the agent can control, within a larger parametric policy
space. After presenting two identification rules (combinatorial and
simplified), applicable under different assumptions on the policy space, we
provide a probabilistic analysis of the simplified one in the case of linear
policies belonging to the exponential family. To improve the performance of our
identification rules, we frame the problem in the recently introduced framework
of the Configurable Markov Decision Processes, exploiting the opportunity of
configuring the environment to induce the agent revealing which parameters it
can control. Finally, we provide an empirical evaluation, on both discrete and
continuous domains, to prove the effectiveness of our identification rules.
