Summary of Survey on Large Language Model-enhanced Reinforcement Learning: Concept, Taxonomy, and Methods, by Yuji Cao et al.
Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods
by Yuji Cao, Huan Zhao, Yuheng Cheng, Ting Shu, Yue Chen, Guolong Liu, Gaoqi Liang, Junhua Zhao, Jinyue Yan, Yun Li
First submitted to arxiv on: 30 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Robotics (cs.RO)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
Summary difficulty | Written by | Summary |
---|---|---|
High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large language models (LLMs) have emerged as a promising approach to augment reinforcement learning (RL) in areas like multi-task learning, sample efficiency, and high-level task planning. This survey provides a comprehensive review of the existing literature on LLM-enhanced RL, summarizing its characteristics compared to conventional RL methods. The authors propose a structured taxonomy categorizing LLMs’ functionalities in RL into four roles: information processor, reward designer, decision-maker, and generator. Each role is analyzed for methodologies, RL challenges mitigated, and future directions. A comparative analysis of each role, potential applications, opportunities, and challenges of LLM-enhanced RL are discussed, with implications for accelerating RL applications in complex domains like robotics, autonomous driving, and energy systems. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can help machines learn faster and better. This paper looks at how these models can be used to improve reinforcement learning (RL). RL is when a computer learns from trying different actions to get a reward. The authors of this paper looked at many studies on using large language models for RL and found that they can help with things like learning multiple tasks at once, needing fewer tries to learn something new, and making better decisions. They also proposed a way to categorize the different ways these models can be used in RL, which could help researchers use them more effectively. |
Keywords
» Artificial intelligence » Multi task » Reinforcement learning