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Temporal Structure

topic1 events
papersSEP 12 04:00 UTC

arXiv Overview Surveys Hierarchical Reinforcement Learning for Temporal Structure

A revised arXiv paper surveys hierarchical reinforcement learning, a subfield aimed at helping agents explore, plan, and learn in complex, open-ended environments. The overview focuses on how temporal structure can be discovered and exploited to make learning more tractable. It frames HRL as a promising direction for building more capable AI agents.

reinforcement-learning/agents/arxiv/research