low-level policy
Sample Complexity of Goal-Conditioned Hierarchical Reinforcement Learning
HRL lead to improved sample complexity, and how much of an improvement can it provide? Theoretical work on sample-complexity bound in Machine Learning has been integral to the development of the field.
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HIQL: Offline Goal-Conditioned RL with Latent States as Actions Seohong Park
We observe that part of this difficulty stems from the "signal-to-noise"
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Language as an Abstraction for Hierarchical Deep Reinforcement Learning
YiDing Jiang, Shixiang (Shane) Gu, Kevin P. Murphy, Chelsea Finn
With the ability to learn concepts and sub-skills that can be composed to solve longer tasks, i.e. hierarchical RL, wecanacquire temporally-extended behaviors.
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