<ul data-eligibleForWebStory="true"><li>Unsupervised skill discovery in reinforcement learning aims to learn diverse behaviors efficiently.</li><li>Existing methods focus on diversity through exploration, mutual information optimization, and temporal representation learning.</li><li>A new regret-aware method is proposed, framing skill discovery as a min-max game of skill generation and policy learning.</li><li>Experimental results demonstrate the method's outperformance of baselines in efficiency and diversity, with a 15% zero-shot improvement in high-dimensional environments.</li></ul>

Efficient Skill Discovery via Regret-Aware Optimization

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