# Hi-Core: Hierarchical Knowledge Transfer for Continual Reinforcement Learning. (arXiv:2401.15098v1 [cs.LG])


Continual reinforcement learning (CRL) empowers RL agents with the ability to
learn from a sequence of tasks, preserving previous knowledge and leveraging it
to facilitate future learning. However, existing methods often focus on
transferring low-level knowledge across similar tasks, which neglects the
hierarchical structure of human cognitive control, resulting in insufficient
knowledge transfer across diverse tasks. To enhance high-level knowledge
transfer, we propose a novel framework named Hi-Core (Hierarchical knowledge
transfer for Continual reinforcement learning), which is structured in two
layers: 1) the high-level policy formulation which utilizes the powerful
reasoning ability of the Large Language Model (LLM) to set

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