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Offline Hierarchical Reinforcement Learning via Inverse Optimization

ICLR 2025 · Reinforcement learning › Offline RL · approach: Offline RL sample complexity theory · 0 citations (OpenAlex)

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Proposes an offline hierarchical RL approach based on inverse optimization to infer unobserved higher-level actions from static datasets; the provided text is only the ICML template, so details are unknown.

Problem

Learning hierarchical policies from static offline datasets, where higher-level actions are not directly observable in the data.

Approach

Uses inverse optimization to recover unobserved high-level actions from offline trajectories so hierarchical policies can be learned offline; details are not present in the supplied text, which is only the ICML template.

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