Summary of Communication-aware Reinforcement Learning For Cooperative Adaptive Cruise Control, by Sicong Jiang et al.
Communication-Aware Reinforcement Learning for Cooperative Adaptive Cruise Control
by Sicong Jiang, Seongjin Choi, Lijun Sun
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO)
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 Reinforcement Learning (RL) has shown promise in optimizing Cooperative Adaptive Cruise Control (CACC) for Connected and Autonomous Vehicles (CAVs), enhancing traffic efficiency and safety. Multi-Agent Reinforcement Learning (MARL) with Centralized Training with Decentralized Execution (CTDE) enables coordinated actions among CAVs, but faces scalability issues when vehicles join or leave the platoon. To address this, we propose Communication-Aware Reinforcement Learning (CA-RL), which includes a communication-aware module that extracts and compresses vehicle communication information through forward and backward information transmission modules. This enables efficient cyclic information propagation within the CACC traffic flow, ensuring policy consistency and mitigating scalability problems. Experimental results show CA-RL outperforms baseline methods in various traffic scenarios, achieving superior scalability, robustness, and overall system performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine self-driving cars working together to make roads safer and more efficient. Reinforcement Learning is a way for computers to learn how to do things better over time. In this case, it helps connected and autonomous vehicles work together to improve traffic flow and safety. The problem is that when new cars join or leave the group, the system can get overwhelmed. To solve this, researchers developed a new method called Communication-Aware Reinforcement Learning. It allows cars to share information with each other in a way that makes the whole system more efficient and reliable. |
Keywords
* Artificial intelligence * Reinforcement learning
