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Subwords as Skills: Tokenization for Sparse-Reward Reinforcement Learning David Y unis TTI-Chicago Chicago, IL
Neural Information Processing Systems
Exploration in sparse-reward reinforcement learning (RL) is difficult due to the need for long, coordinated sequences of actions in order to achieve any reward.
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Knowledge Composition using Task Vectors with Learned Anisotropic Scaling Frederic Z. Zhang Paul Albert
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