Loop-Free Inverse Reinforcement Learning via Sequential Value Recovery with Q-Score Matching
Inverse Reinforcement Learning (IRL) aims to recover a reward function that explains expert demonstrations. Existing IRL methods typically rely on a bi-level optimization procedure that alternates between reward learning and policy optimization, leading to substantial computational burden and training instability. In t...
Yang Chen, Yi-Tan Zhang, Michael J. Witbrock et al.
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