Summary of Large Language Model-enhanced Reinforcement Learning For Generic Bus Holding Control Strategies, by Jiajie Yu et al.
Large Language Model-Enhanced Reinforcement Learning for Generic Bus Holding Control Strategies
by Jiajie Yu, Yuhong Wang, Wei Ma
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 an automatic reward generation paradigm for Reinforcement Learning (RL) in bus holding control systems. The proposed method, called LLM-enhanced RL, utilizes Large Language Models (LLMs) to generate rewards that optimize the cumulative reward function. This approach addresses the challenge of translating sparse and delayed control goals into dense and real-time rewards. The LLM-based modules include a reward initializer, modifier, performance analyzer, and refiner, which cooperate to improve the reward function based on training and test results. The paper applies this paradigm to various bus holding control scenarios, including a synthetic single-line system and a real-world multi-line system, demonstrating its superiority and robustness compared to vanilla RL strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses Reinforcement Learning (RL) to help buses stay stable and efficient. Right now, it’s hard to create rewards for RL because they need to be dense and in real-time, but the data is sparse and delayed. To fix this, the researchers created a new way called LLM-enhanced RL that uses Large Language Models (LLMs) to generate rewards. The LLM helps make the reward function better by looking at how well it does during training and testing. This approach is tested on different bus systems and shows that it works better than other methods. |
Keywords
* Artificial intelligence * Reinforcement learning
