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Hierarchical reinforcement learning splits the policy into multiple layers, with the upper layer outputting abstract intents or subgoals and the lower layer handling concrete execution, used to alleviate the problems of an overly large action space and sparse exploration in long-horizon tasks, but each layer needs its own credit assignment signal, making training less stable than a single-layer policy.
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10/13/2026
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