Summary of Offlight: An Offline Multi-agent Reinforcement Learning Framework For Traffic Signal Control, by Rohit Bokade et al.
OffLight: An Offline Multi-Agent Reinforcement Learning Framework for Traffic Signal Control
by Rohit Bokade, Xiaoning Jin
First submitted to arxiv on: 10 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Multiagent Systems (cs.MA)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 offline MARL framework, OffLight, addresses the challenges of traditional traffic control systems by leveraging historical traffic data for training. It incorporates Importance Sampling (IS) to correct for distributional shifts and Return-Based Prioritized Sampling (RBPS) to focus on high-quality experiences. OffLight utilizes a Gaussian Mixture Variational Graph Autoencoder (GMM-VGAE) to capture the diverse distribution of behavior policies from local observations. Experimental results show that OffLight outperforms existing offline RL methods, achieving significant reductions in average travel time and queue length. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary OffLight is a new way to improve traffic control by using old data to train a smart system. This system can handle different types of traffic behavior and make better decisions because of it. It’s like having a smart traffic manager that learns from the past to make the roads smoother for everyone. |
Keywords
* Artificial intelligence * Autoencoder
