<ul><li>Inverse Reinforcement Learning (IRL) aims to retrieve reward function from expert demonstrations.</li><li>Recently, Optimal Transport (OT) methods have been effective but come with complexities.</li><li>A new study challenges the necessity of OT in IRL with two simpler alternatives: Minimum-Distance Reward and Segment-Matching Reward.</li><li>Extensive evaluations show these simple methods match or surpass recent OT-based approaches, indicating a reevaluation of complexity in future IRL design.</li></ul>

Is Optimal Transport Necessary for Inverse Reinforcement Learning?

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