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Research questionHow can hierarchical reinforcement learning use incrementally acquired knowledge for long-horizon exploration with sparse rewards?In fixed-knowledge HRL, information discovered during exploration does not readily alter the high-level structure used to choose subgoals. This makes long-horizon exploration inefficient when rewards are sparse and useful behavior must be learned from limited experience.
Reasoning
Reinforcement Learning
Latest papersRecent research connected to this question, newest first.Neurosymbolic Reasoning with Incremental Knowledge for Sample Efficient Hierarchical Reinforcement LearningThe source studies neurosymbolic HRL for navigation, with symbolic high-level planning over an updatable incremental-knowledge representation and low-level goal-conditioned neural modules that learn motion primitives through reward shaping. It reports navigation experiments and develops Belief World Tree Search for optimal symbolic planning when prior world knowledge is available.research paper · Sep 3, 2026
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