New method estimates follower rewards in bilevel reinforcement learning

Researchers have developed a new method called Reward-Estimated Hypergradients (RE-HG) to address bilevel reinforcement learning in scenarios where the follower's reward function is unknown. RE-HG uses inverse reinforcement learning (IRL) to estimate the follower's reward from observed trajectories. The method is proven effective for linear rewards, where biases from IRL are mitigated by aggregating estimates across diverse dynamics. For finite samples, RE-HG stabilizes estimation by truncating small eigenvalues, and experiments show it performs comparably to a white-box oracle while avoiding local optima. AI

IMPACT Introduces a novel approach for bilevel reinforcement learning in black-box settings, potentially improving agent decision-making when reward functions are not explicitly known.

RANK_REASON The item describes a new method and its experimental validation in a research paper. [lever_c_demoted from research: ic=1 ai=1.0]

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• bilevel reinforcement learning
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• Hypergradient
• inverse reinforcement learning
• RE-HG
• reinforcement learning
• Reward-Estimated Hypergradients

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