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This paper introduces Trust Region Inverse Reinforcement Learning (TRIRL), a method that combines monotonic dual improvement with efficient local policy updates to outperform state-of-the-art imitation learning methods. It addresses the trade-off between stability and computational cost in IRL by using trust-region constraints.