GIST: Guided Interpretable Large Language Model Strategy Transfer for Multi-Task Reinforcement Learning

Abstract

Multi-task reinforcement learning (MTRL) presents critical challenges, such as the complexities of task-switching and maintaining knowledge retention across various tasks, especially within control tasks. These challenges frequently result in sub-optimal decision-making and catastrophic forgetting during task transitions. To mitigate these challenges, we introduce a framework that leverages large language models for strategic guidance, optimizing decision-making and action selection. The framework integrates a Kolmogorov-Arnold Network to ensure interpretability via symbolic regression, enabling effective strategy transfer across diverse environments. Adaptive Elastic Weight Consolidation is employed to prevent catastrophic forgetting, preserving task-specific knowledge across training. A caching mechanism reduces computational overhead linked to large language model queries, enhancing overall efficiency. Experimental evaluations indicate competitive performance, robust interpretability, and effective strategy transfer.