Stage Innovation : Ingénieur Intelligence Artificielle / Reinforcement Learning
Core
Develop a World Model using Reinforcement Learning to predict traffic evolution and optimize traffic light coordination in metropolitan areas.
Role type
Intern, Reinforcement Learning Engineer (Traffic Simulation)
Builds
Traffic simulation models and optimization strategies for smart traffic lights
Domain
Transportation / AI / Reinforcement Learning
Deliverable
production ML models
Required skills
Python, Applied Mathematics, Deep Learning, Reinforcement Learning, Multi-Agent Systems
Preferred skills
Research experience (academic projects, lab internships)
Technologies
SUMO
Responsibilities
Study and characterize traffic scenarios where global dynamics anticipation is advantageous; Design a World Model to learn and predict traffic evolution from observed states in SUMO; Evaluate the impact of learned latent representations on agent cooperation and congestion management
Seniority
Intern (final year engineering student)
