Summary of Generating and Evolving Reward Functions For Highway Driving with Large Language Models, by Xu Han et al.
Generating and Evolving Reward Functions for Highway Driving with Large Language Models
by Xu Han, Qiannan Yang, Xianda Chen, Xiaowen Chu, Meixin Zhu
First submitted to arxiv on: 15 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Neural and Evolutionary Computing (cs.NE); Robotics (cs.RO)
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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 novel framework combines Large Language Models (LLMs) with Reinforcement Learning (RL) to simplify the design of reward functions for autonomous driving. By leveraging LLMs’ coding capabilities, the framework generates and evolves reward function codes based on driving environment and task descriptions. The process involves iterative cycles of RL training and LLM reflection, utilizing their ability to review and improve output. A specific prompt template is also developed to ensure effective and error-free code generation. Experimental results in a highway driving simulator demonstrate that this method outperforms expert handcrafted reward functions, achieving a 22% higher average success rate. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses computers to help create the rules for self-driving cars. Right now, making these rules is a tricky process that people do manually. The new approach combines two technologies: large language models (which are good at writing code) and reinforcement learning (which helps computers learn from trying things). This combination allows the computer to generate and improve its own rules for driving. The researchers tested this method in a simulated highway scenario and found it worked better than what experts had come up with on their own. |
Keywords
» Artificial intelligence » Prompt » Reinforcement learning
