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arXiv · 2307.11373

Offline Diversity Maximization Under Imitation Constraints

Marin Vlastelica·Jin Cheng·Georg Martius·Pavel Kolev
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Abstract

There has been significant recent progress in the area of unsupervised skill discovery, utilizing various information-theoretic objectives as measures of diversity. Despite these advances, challenges remain: current methods require significant online interaction, fail to leverage vast amounts of available task-agnostic data and typically lack a quantitative measure of skill utility. We address these challenges by proposing a principled offline algorithm for unsupervised skill discovery that, in addition to maximizing diversity, ensures that each learned skill imitates state-only expert demonstrations to a certain degree. Our main analytical contribution is to connect Fenchel duality, reinforcement learning, and unsupervised skill discovery to maximize a mutual information objective subject to KL-divergence state occupancy constraints. Furthermore, we demonstrate the effectiveness of our method on the standard offline benchmark D4RL and on a custom offline dataset collected from a 12-DoF quadruped robot for which the policies trained in simulation transfer well to the real robotic system.

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Marin Vlastelica, Jin Cheng, Georg Martius, Pavel Kolev. 2024-06-21. Offline Diversity Maximization Under Imitation Constraints. https://arxiv.org/abs/2307.11373

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