Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods

arXiv:2404.00282v1 Announce Type: new
Abstract: With extensive pre-trained knowledge and high-level general capabilities, large language models (LLMs) emerge as a promising avenue to augment reinforcement learning (RL) in aspects such as multi-task learning, sample efficiency, and task planning. In this survey, we provide a comprehensive review of the existing literature in $\textit{LLM-enhanced RL}$ and summarize its characteristics compared to conventional RL methods, aiming to clarify the research scope and directions for future studies. Utilizing the classical agent-environment interaction paradigm, we …

abstract arxiv capabilities concept cs.ai cs.cl cs.lg cs.ro efficiency general knowledge language language model language models large language large language model large language models llms multi-task learning planning reinforcement reinforcement learning review sample survey taxonomy type