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ConceptHierarchical RL

Hierarchical Reinforcement Learning

Hierarchical reinforcement learning decomposes a policy into multiple levels: higher levels output abstract intentions or subgoals, while lower levels handle concrete execution. This structure mitigates the problems of an excessively large action space and sparse exploration in long-horizon tasks.

Timeline (last 90 days)

Oct 13

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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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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Source Articles

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