Summary of Traffic Expertise Meets Residual Rl: Knowledge-informed Model-based Residual Reinforcement Learning For Cav Trajectory Control, by Zihao Sheng et al.
Traffic expertise meets residual RL: Knowledge-informed model-based residual reinforcement learning for CAV trajectory control
by Zihao Sheng, Zilin Huang, Sikai Chen
First submitted to arxiv on: 30 Aug 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 Model-based reinforcement learning (RL) is expected to exhibit higher sample efficiency compared to model-free RL by utilizing a virtual environment model. However, it’s challenging to obtain sufficiently accurate representations of the environmental dynamics due to uncertainties in complex systems and environments. An inaccurate environment model may degrade the sample efficiency and performance of model-based RL. Furthermore, while model-based RL can improve sample efficiency, it often still requires substantial training time to learn from scratch, potentially limiting its advantages over model-free approaches. To address these challenges, this paper introduces a knowledge-informed model-based residual reinforcement learning framework that enhances learning efficiency by infusing established expert knowledge into the learning process and avoiding the issue of beginning from zero. The approach integrates traffic expert knowledge into a virtual environment model, employing the Intelligent Driver Model (IDM) for basic dynamics and neural networks for residual dynamics, thus ensuring adaptability to complex scenarios. The proposed strategy combines traditional control methods with residual RL, facilitating efficient learning and policy optimization without the need to learn from scratch. Experimental results demonstrate that the proposed approach enables a CAV agent to achieve superior performance in trajectory control compared to baseline agents in terms of sample efficiency, traffic flow smoothness, and traffic mobility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Model-based reinforcement learning (RL) is a way for machines to learn by using virtual environments. This helps them learn faster than other methods. But it’s hard to get the environment right because complex systems are tricky. If the environment isn’t accurate, it can hurt performance. To make things better, this paper introduces a new way to use expert knowledge to help with learning. It uses traffic experts’ knowledge and combines it with machine learning. This helps machines learn faster and make better decisions. The approach is tested on CAV (Connected and Automated Vehicles) trajectory control tasks. The results show that the new method does a great job of controlling traffic flow and making sure vehicles move smoothly. It also shows that this method is more efficient than other methods, which means it can do things faster without sacrificing performance. |
Keywords
» Artificial intelligence » Machine learning » Optimization » Reinforcement learning
