{"product_id":"kit-pi-5-reinforcement-learning-traffic-controller","title":"Pi 5 Reinforcement Learning Traffic Controller","description":"\u003ch1\u003eRaspberry Pi 5 Multi-Agent RL Traffic Controller Kit — Train AI to Coordinate Intersections Without Human Rules\u003c\/h1\u003e\n\n\u003cp class=\"value-summary\"\u003eEvery part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.\u003c\/p\u003e\n\n\u003cdiv class=\"specs-strip\"\u003e\n  \u003cspan\u003e\u003cstrong\u003eDifficulty:\u003c\/strong\u003e Advanced\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eBuild Time:\u003c\/strong\u003e 10-12 hrs\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eAge:\u003c\/strong\u003e 18-25\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eSkill:\u003c\/strong\u003e Multi-agent reinforcement learning\u003c\/span\u003e\n\u003c\/div\u003e\n\n\u003cp\u003eToday’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.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You’ll Build\u003c\/h2\u003e\n\u003cp\u003eYou 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.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You’ll Learn\u003c\/h2\u003e\n\u003cul\u003e\n  \u003cli\u003eImplement multi‑agent RL algorithms (Independent Q‑Learning, MADDPG) on edge hardware and tune reward functions for traffic optimisation.\u003c\/li\u003e\n  \u003cli\u003eInterface IR sensors and ESP32 microcontrollers to build a low‑latency data pipeline simulating real‑time vehicle detection.\u003c\/li\u003e\n  \u003cli\u003eTrain neural networks directly on Raspberry Pi 5 using GPU‑accelerated frameworks and manage large replay buffers on the included NVMe SSD.\u003c\/li\u003e\n  \u003cli\u003eAnalyse emergent coordination between self‑interested agents — evaluate metrics like system throughput and average delay without explicit communication.\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2\u003eKit Contents\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eComponent\u003c\/th\u003e\n\u003cth\u003eQuantity\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eRaspberry Pi 5 8GB\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eNVMe SSD 512GB\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003ePi 5 M.2 HAT+\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eIR Sensor Module\u003c\/td\u003e\n\u003ctd\u003e8\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eESP32 Dev Board\u003c\/td\u003e\n\u003ctd\u003e2\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eUSB-C PSU\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eMicroUSB Cable\u003c\/td\u003e\n\u003ctd\u003e2\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eM-M Wires\u003c\/td\u003e\n\u003ctd\u003e30\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n\n\u003ch2\u003eWhy Buy This Kit Instead of Sourcing Parts Separately\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eFactor\u003c\/th\u003e\n\u003cth\u003eSourcing Separately\u003c\/th\u003e\n\u003cth\u003eCompoden Kit\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eCompatibility checks\u003c\/td\u003e\n\u003ctd\u003eYou verify every part\u003c\/td\u003e\n\u003ctd\u003ePre-tested as a system\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eBuild support\u003c\/td\u003e\n\u003ctd\u003eForums and scattered tutorials\u003c\/td\u003e\n\u003ctd\u003eAI companion trained on this exact project\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eTime to first working build\u003c\/td\u003e\n\u003ctd\u003eDays of debugging\u003c\/td\u003e\n\u003ctd\u003eHours, with step-by-step guidance\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eShipping coordination\u003c\/td\u003e\n\u003ctd\u003eMultiple sellers, multiple delays\u003c\/td\u003e\n\u003ctd\u003eOne shipment from Bengaluru in 3-5 days\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n\n\u003ch2\u003eWho This Kit Is For\u003c\/h2\u003e\n\u003cp\u003eFinal‑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.\u003c\/p\u003e\n\n\u003ch2\u003eBuilt and Backed by Compoden\u003c\/h2\u003e\n\u003cp\u003eEvery 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.\u003c\/p\u003e\n\n\u003cdetails\u003e\u003csummary\u003eWhat if I get stuck during the build?\u003c\/summary\u003e\u003cp\u003eOpen the AI companion on your phone or laptop — it understands every step of sensor wiring, MARL code debugging, and hyperparameter tuning. You can also message our support on WhatsApp for a human response within hours.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eDoes the kit include a physical traffic junction model?\u003c\/summary\u003e\u003cp\u003eNo, you’ll create the intersection layout using a breadboard and cardboard or a 3D‑printed base. We provide detailed circuit diagrams and a laser‑cut template you can download — the IR sensors mount exactly as shown.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eCan I experiment with different RL algorithms on this kit?\u003c\/summary\u003e\u003cp\u003eAbsolutely. The Pi 5’s GPU‑enabled PyTorch environment supports DQN, A2C, PPO, and custom multi‑agent approaches. The large SSD holds experience replay data for offline training runs, so you can test multiple strategies without starting from scratch.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eHow do I visualise the agents’ performance live?\u003c\/summary\u003e\u003cp\u003eA companion GitHub repository includes a Python dashboard that launches a local web server showing real‑time plots of waiting vehicles, agent actions, and cumulative wait time. You can even stream the data to a laptop for presentations.\u003c\/p\u003e\u003c\/details\u003e\n\n\u003cdiv class=\"kit-description\"\u003e\n  \u003cp\u003eMulti-intersection traffic signal control via MARL on Pi 5 — agents learn to minimise cumulative wait time without communication.\u003c\/p\u003e\n  \u003ch4\u003eWhat's in this kit\u003c\/h4\u003e\n  \u003cul\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-5-model-b-8gb-high-performance-single-board-computer\"\u003eRaspberry Pi 5 8GB\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/official-raspberry-pi-m2-hat-nvme-ssd-add-on-board-for-pi-5\"\u003eNVMe SSD 512GB\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-5-pcie-to-m2-nvme-ssd-expansion-board-by-elecrow\"\u003ePi 5 M.2 HAT+\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\n\u003ca href=\"\/products\/9-in-1-arduino-sensor-kit-with-ultrasonic-pir-dht11-mq2-more\"\u003eIR Sensor Module\u003c\/a\u003e x8\u003c\/li\u003e\n    \u003cli\u003e\n\u003ca href=\"\/products\/esp32-30-pin-development-board-cp2102-wifi-bluetooth\"\u003eESP32 Dev Board\u003c\/a\u003e x2\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-4-official-power-supply-5v-3a-usb-c-compoden\"\u003eUSB-C PSU\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\n\u003ca href=\"\/products\/microusb-cable-1m-charging-data-cord-for-arduino-android\"\u003eMicroUSB Cable\u003c\/a\u003e x2\u003c\/li\u003e\n    \u003cli\u003eM-M Wires x30\u003c\/li\u003e\n  \u003c\/ul\u003e\n\u003c\/div\u003e\n\n\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What is included in the Pi 5 Reinforcement Learning Traffic Controller?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"The Pi 5 Reinforcement Learning Traffic Controller includes all components needed: Raspberry Pi 5 8GB, NVMe SSD 512GB, Pi 5 M.2 HAT+, IR Sensor Module, ESP32 Dev Board and more. Everything is pre-tested for compatibility and shipped from Bengaluru, India.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What skill level is required for the Pi 5 Reinforcement Learning Traffic Controller?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"This kit is designed for Advanced level makers, suitable for ages 18-25. Multi-intersection traffic signal control via MARL on Pi 5 — agents learn to minimise cumulative wait time without communication. Estimated build time is 10-12 hrs.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I buy the Pi 5 Reinforcement Learning Traffic Controller online in India?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes, the Pi 5 Reinforcement Learning Traffic Controller is available online at Compoden (compoden.in), India's AI-powered electronics and robotics store. 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