arXiv Machine Learning October 23, 2026 By Alireza Modirshanechi, Benjamin Eysenbach, Peter Dayan, Eric Schulz

Unifying Goal-Conditioned RL and Unsupervised Skill Learning via Control-Maximization

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The paper unifies goal‑conditioned reinforcement learning (GCRL) and mutual information skill learning (MISL) under a control‑maximization framework. It shows that three canonical GCRL formulations are fundamentally inequivalent, yet all can be interpreted as maximizing goal sensitivity of future trajectories. By relating MISL objectives to these GCRL formulations, the authors prove that each MISL objective is bounded by a formulation‑specific downstream goal sensitivity, establishing a precise correspondence between skill diversity and goal‑reachability.

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