Optimizing Traffic Lights with Multi-agent Deep Reinforcement Learning and V2X communication

Understand

We consider a system to optimize duration of traffic signals using multi-agent deep reinforcement learning and Vehicle-to-Everything (V2X) communication.

• This system aims at analyzing independent and shared rewards for multi-agents to control duration of traffic lights.
• A learning agent traffic light gets information along its lanes within a circular V2X coverage.
• The duration cycles of traffic light are modeled as Markov decision Processes.