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#inverse-rl

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papersSEP 11 04:00 UTC

Statistical Analysis of Inverse Entropy-Regularized Reinforcement Learning

A new arXiv paper examines inverse reinforcement learning, where the goal is to recover the reward function that best explains an expert's observed state-action trajectories. The authors focus on the long-standing problem that classical IRL methods can return multiple, non-unique reward functions, and provide a statistical treatment of an entropy-regularized formulation.

reinforcement-learning/inverse-rl/research/arxiv