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Disentangled Unsupervised Skill Discovery for Efficient Hierarchical Reinforcement Learning

Neural Information Processing Systems

However, existing unsupervised skill discovery methods often learn entangled skills where one skill variable simultaneously influences many entities in the environment, making downstream skill chaining extremely challenging.



Convergence of No-Swap-Regret Dynamics in Self-Play

Neural Information Processing Systems

Despite growing interest in understanding and predicting the long-term behavior of such systems, recent studies have revealed a wide array of negative results, demonstrating the elusiveness of game dynamics.