Raspberry Pi 5 Multi-Agent RL Traffic Controller Kit — Train AI to Coordinate Intersections Without Human Rules

Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.

Today’s traffic lights still rely on fixed timers or simple sensor logic, creating unnecessary congestion and endless wait times. This kit puts you at the forefront of smart-city research: build a multi-agent reinforcement learning (MARL) system where each intersection approach is controlled by an AI agent that learns to minimise cumulative wait time — entirely without pre‑programmed rules or communication between agents. On the Raspberry Pi 5, you’ll train neural networks that observe traffic from eight IR sensors, then independently decide green-light durations to outperform any human-tuned signal plan.

What You’ll Build

You will create a physical, sensor‑rich model of a 4‑way intersection. Eight IR sensor modules detect vehicles in each lane, while two ESP32 boards collect the raw data and stream it over WiFi to the Pi 5. On the Pi, a Python environment runs multiple RL agents (one per approach) using PyTorch. Without centralised control, the agents learn to recognise congestion patterns and coordinate actions purely by observing the shared traffic state. The final system adapts in real time, prioritises heavier flows, and dramatically cuts average wait time compared to fixed‑time signals — a tangible demonstration of decentralised AI for smart cities.

What You’ll Learn

• Implement multi‑agent RL algorithms (Independent Q‑Learning, MADDPG) on edge hardware and tune reward functions for traffic optimisation.
• Interface IR sensors and ESP32 microcontrollers to build a low‑latency data pipeline simulating real‑time vehicle detection.
• Train neural networks directly on Raspberry Pi 5 using GPU‑accelerated frameworks and manage large replay buffers on the included NVMe SSD.
• Analyse emergent coordination between self‑interested agents — evaluate metrics like system throughput and average delay without explicit communication.

Kit Contents

Why Buy This Kit Instead of Sourcing Parts Separately

Who This Kit Is For

Final‑year B.Tech students in ECE, EEE, or CSE exploring Smart City thesis projects, Smart India Hackathon teams that need a real‑time AI build on edge hardware, and researchers at IITs, NITs, VIT, or BITS Pilani prototyping decentralised traffic control. The advanced MARL approach also gives CBSE Class 12 AI elective students a challenging, portfolio‑grade project that pushes far beyond textbook exercises.

Built and Backed by Compoden

Every Compoden kit ships with an AI build companion trained on this exact project — accessible via a QR code on the box, with WhatsApp and email backup. We've spent 10 years building projects for makers, schools, and institutions across India. If a part fails because of a manufacturing defect, replace it free within 7 days.