Summary of Real-time System Optimal Traffic Routing Under Uncertainties — Can Physics Models Boost Reinforcement Learning?, by Zemian Ke et al.
Real-time system optimal traffic routing under uncertainties – Can physics models boost reinforcement learning?
by Zemian Ke, Qiling Zou, Jiachao Liu, Sean Qian
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
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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 algorithm, TransRL, is designed to optimize traffic routing in transportation systems by integrating reinforcement learning with physics models. This approach aims to improve performance, reliability, and interpretability by leveraging interactions with the environment and insights from physics models. The algorithm begins by establishing a deterministic policy grounded in physics models, which is then learned from and guided by a differentiable and stochastic teacher policy. During training, TransRL maximizes cumulative rewards while minimizing the Kullback Leibler (KL) divergence between the current policy and the teacher policy. This enables TransRL to adapt to uncertain demands and unknown system dynamics, particularly in expansive transportation networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TransRL is a new algorithm that helps find the best route for traffic to reduce congestion. It uses a combination of learning from data and understanding how physics works to make better decisions. The approach starts with a set of rules based on physics, then learns from data to improve its performance. This allows it to handle uncertain demands and unknown system dynamics well, especially in large transportation networks. The algorithm was tested on three different transportation networks, and the results show that it outperforms other algorithms in terms of adaptability and reliability. |
Keywords
* Artificial intelligence * Reinforcement learning
