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 goals and 2) the
low-level policy learning through RL which is oriented by high-level goals.
Moreover, the knowledge base (policy library) is constructed to store policies
that can be retrieved for hierarchical knowledge transfer. Experiments
conducted in MiniGrid have demonstrated the effectiveness of Hi-Core in
handling diverse CRL tasks, outperforming popular baselines.

Hi-Core 是一个新颖框架，用于在连续强化学习中增强高层次的知识传递，并通过大型语言模型（LLM）进行目标设置和低层次策略学习，并通过知识库存储策略实现层次化知识传递，实验证明其在处理多样化 CRL 任务上的有效性优于常见的基线模型。