Summary of Human-centric Reward Optimization For Reinforcement Learning-based Automated Driving Using Large Language Models, by Ziqi Zhou et al.
Human-centric Reward Optimization for Reinforcement Learning-based Automated Driving using Large Language Models
by Ziqi Zhou, Jingyue Zhang, Jingyuan Zhang, Yangfan He, Boyue Wang, Tianyu Shi, Alaa Khamis
First submitted to arxiv on: 7 May 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 This paper introduces a novel approach to optimize Reinforcement Learning (RL) reward functions in Automated Driving (AD) agents using large language models (LLMs). The proposed framework takes instructions and dynamic environment descriptions as input, which the LLM uses to generate rewards that steer RL agents towards human-like behavior. Experimental results show improved performance and anthropomorphic behavior. Strategies for reward-proxy and reward-shaping are also explored, highlighting the significance of prompt design in shaping AD vehicle behavior. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a way to make Reinforcement Learning (RL) better for Autonomous Driving (AD). It uses special language models to help RL find the right rewards, making the car drive more like humans. The results show that this approach works and makes the driving more human-like. The researchers also looked at different ways to design these rewards, showing how important it is to get them just right. |
Keywords
» Artificial intelligence » Prompt » Reinforcement learning
