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This paper introduces Interactive Inverse Reinforcement Learning (IIRL), a framework where a learner actively interacts with an expert to infer reward functions, formulated as a stochastic bi-level optimization problem. The authors propose the BISIRL algorithm, providing convergence guarantees and experimental validation for this interactive learning paradigm.