Summary of Lmgt: Optimizing Exploration-exploitation Balance in Reinforcement Learning Through Language Model Guided Trade-offs, by Yongxin Deng et al.
LMGT: Optimizing Exploration-Exploitation Balance in Reinforcement Learning through Language Model Guided Trade-offs
by Yongxin Deng, Xihe Qiu, Xiaoyu Tan, Wei Chu, Yinghui Xu
First submitted to arxiv on: 7 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel framework called Language Model Guided Trade-offs (LMGT) to optimize Reinforcement Learning (RL) processes. LMGT leverages Large Language Models (LLMs) to process non-standard data forms and manage the exploration-exploitation trade-off, improving sample efficiency. The framework is tested across various RL tasks and deployed in industrial-grade recommendation systems, outperforming baseline methods. By utilizing prior knowledge embedded in LLMs, LMGT reduces the time cost required during training. This approach can be applied to scenarios with sparse rewards, making it particularly useful for optimizing computational resources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to make computer learning more efficient. It uses big language models to help agents learn faster and better. The model is tested on different tasks and works well in real-world applications. This makes it possible to use computers to help us make decisions, like choosing what movie to watch or which product to buy. |
Keywords
» Artificial intelligence » Language model » Reinforcement learning
