Survey on large language model-enhanced reinforcement learning: Concept, taxonomy, and methods
Open accessAbstract
With extensive pretrained knowledge and high-level general capabilities, large language models (LLMs) emerge as a promising avenue to augment reinforcement learning (RL) in aspects, such as multitask learning, sample efficiency, and high-level task planning. In this survey, we provide a comprehensive review of the existing literature in LLM-enhanced RL and summarize its characteristics compared with 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. For each role, we summarize the methodologies, analyze the specific RL challenges that are mitigated and provide insights into future directions. Finally, the comparative analysis of each role, potential applications, prospective opportunities, and challenges of the LLM-enhanced RL are discussed. By proposing this taxonomy, we aim to provide a framework for researchers to effectively leverage LLMs in the RL field, potentially accelerating RL applications in complex applications, such as robotics, autonomous driving, and energy systems.
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Framework implementations support comprehensive ecosystems: AutoGen enables dynamic response generation, MetaGPT provides shared message pools, CAMEL offers integrated orchestration, CrewAI facilitates adaptation, with reinforcement learning integration enhancing reward redesign, action selection, and policy interpretation.
“Framework implementations support comprehensive ecosystems: AutoGen enables dynamic response generation, MetaGPT provides shared message pools, CAMEL offers integrated orchestration, CrewAI facilitates adaptation, with reinforcement learning integration enhancing reward redesign, action selection, and policy interpretation [188, 38, 119, 1004, 228, 871, 935, 958, 1273].”
From A Survey of Context Engineering for Large Language Models (2025) · cites this paper as [119]
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Self-citationInductive biases for zero-shot systematic generalization in language-informed reinforcement learning
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Adaptive Predefined Performance Control Using Reinforcement Learning for a Single-DOF Manipulator
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- Authors
- Cao, Y., Zhao, H., Cheng, Y., Shu, T., Chen, Y., Liu, G., Liang, G., Zhao, J., Yan, J., & Li, Y.
- Affiliations
- Chinese University of Hong Kong (HK)
- Hong Kong Polytechnic University (HK)
- Chinese University of Hong Kong, Shenzhen (CN)
- Shenzhen University (CN)
- Nanyang Technological University (SG)
- Harbin Institute of Technology (CN)
- University of Electronic Science and Technology of China (CN)
- ORCID
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2025-06
- Volume
- 36
- Issue
- 6
- Pages
- 9737-9757
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W4393763811
- Cited by
- 174
- References
- 176
- Citations by year
- 2026: 87 · 2025: 79 · 2024: 8
- FWCI
- 25.68
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Neural Networks and Learning Systems (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DR-NTU (Nanyang Technological University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (72331009, 72171206, 92270105)
- Guangdong Power Grid Company (GDKJXM20231024)
- Shenzhen Key Lab of Crowd Intelligence Empowered Low-Carbon Energy Network (ZDSYS20220606100601002)
- Shenzhen Natural Science Fund (GXWD20231128112434001)
- Shenzhen Institute of Artificial Intelligence and Robotics for Society
- PolyU Direct Grant (P0047700, P0043885, P0051105)
- CUHK Strategic Partnership Award for Research Collaboration (4750467)
- CUHK Direct Grant for Research (4055228)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- large language models, reinforcement learning, reward function design, multi-task learning, sample efficiency, task planning, agent-environment interaction, robotics, autonomous driving, energy systems
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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