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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. Weintroduce an approach based on statistical testing to identify the set ofpolicy parameters the agent can control, within a larger parametric policyspace. After presenting two identification rules (combinatorial andsimplified), applicable under different assumptions on the policy space, weprovide a probabilistic analysis of the simplified one in the case of linearpolicies belonging to the exponential family. To improve the performance of ouridentification rules, we frame the problem in the recently introduced frameworkof the Configurable Markov Decision Processes, exploiting the opportunity ofconfiguring the environment to induce the agent revealing which parameters itcan control. Finally, we provide an empirical evaluation, on both discrete andcontinuous domains, to prove the effectiveness of our identification rules.

Policy Space Identification in Configurable Environments