  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 propose a structured taxonomy to
systematically categorize LLMs' functionalities in RL, including four roles:
information processor, reward designer, decision-maker, and generator.
Additionally, for each role, we summarize the methodologies, analyze the
specific RL challenges that are mitigated, and provide insights into future
directions. Lastly, potential applications, prospective opportunities and
challenges of the $\textit{LLM-enhanced RL}$ are discussed.
