A naukri.com initiative
Arxiv
1w
262
Image Credit: Arxiv
A Constrained Multi-Agent Reinforcement Learning Approach to Autonomous Traffic Signal Control
- A Constrained Multi-Agent Reinforcement Learning Approach to Autonomous Traffic Signal Control
- Traffic congestion in modern cities is exacerbated by the limitations of traditional fixed-time traffic signal systems.
- Adaptive Traffic Signal Control (ATSC) algorithms dynamically adjust signal timing based on real-time traffic conditions.
- The proposed algorithm, Multi-Agent Proximal Policy Optimization with Lagrange Cost Estimator (MAPPO-LCE), integrates the Lagrange multipliers method to balance rewards and constraints, improving traffic signal control policies.
Read Full Article
15 Likes
For uninterrupted reading, download the app