Summary of Llm-empowered State Representation For Reinforcement Learning, by Boyuan Wang et al.
LLM-Empowered State Representation for Reinforcement Learning
by Boyuan Wang, Yun Qu, Yuhang Jiang, Jianzhun Shao, Chang Liu, Wenming Yang, Xiangyang Ji
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 proposed LLM-Empowered State Representation (LESR) approach leverages large language models to generate task-related state representation codes, enhancing the continuity of network mappings and facilitating efficient training. Traditional methods rely on extensive sample learning, leading to low sample efficiency and high time costs. In contrast, LESR demonstrates high sample efficiency, outperforming state-of-the-art baselines by an average of 29% in accumulated reward and 30% in success rates across Mujoco and Gym-Robotics tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to help robots learn from experience. Currently, robots struggle to understand what’s happening around them, which makes it hard for them to make good decisions. To solve this problem, the team used a type of AI called large language models (LLMs). These LLMs can learn lots of information without being explicitly told what to do. The researchers then combined these LLMs with existing robot learning methods to create a new approach called LESR. This approach helps robots understand their environment better and make more informed decisions. |
