{"product_id":"kit-arduino-q-learning-line-follower","title":"Arduino Q-Learning Line Follower Kit with Arduino Uno + Motor","description":"\u003ch1\u003eArduino Q-Learning Line Follower Kit: Build an AI Robot That Learns to Follow Lines\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 Beginner\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eBuild Time:\u003c\/strong\u003e 3-4 hrs\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eAge:\u003c\/strong\u003e 15-18\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eSkill:\u003c\/strong\u003e AI Reinforcement Learning\u003c\/span\u003e\n\u003c\/div\u003e\n\n\u003cp\u003eInstead of hard-coding a PID controller, you’ll train a Q-learning agent directly on the Arduino to map IR sensor states to motor commands. Over 20 laps it discovers the most efficient line-following strategy, smoothing turns and correcting drifts that traditional algorithms miss. This kit brings real reinforcement learning to your desk, no cloud, no external computer — just the robot and its evolving Q-table.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Build\u003c\/h2\u003e\n\u003cp\u003eYou’ll assemble a two-wheeled robot carrying a three-sensor line array, then flash a reinforcement learning sketch that populates a Q-table as it runs. Watch the behavior improve lap by lap: early wobbles give way to confident tracking, and the robot learns to recover from sharp corners it could not handle at the start. By the final lap it follows any black line on a white surface with minimal deviation.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Learn\u003c\/h2\u003e\n\u003cul\u003e\n  \u003cli\u003eImplement a Q-learning reinforcement learning algorithm on a microcontroller with limited RAM\u003c\/li\u003e\n  \u003cli\u003eMap analog IR sensor readings to discrete states for efficient learning and table lookup\u003c\/li\u003e\n  \u003cli\u003eTune reward functions and balance exploration\/exploitation trade-offs in a physical system\u003c\/li\u003e\n  \u003cli\u003eDiagnose sensor noise, motor latency, and battery droop that affect learning convergence\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\u003eArduino Uno R3\u003c\/td\u003e\n\u003ctd\u003ex1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eTCRT5000 IR Module\u003c\/td\u003e\n\u003ctd\u003ex3\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eL298N Motor Driver\u003c\/td\u003e\n\u003ctd\u003ex1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eTT Gear Motor\u003c\/td\u003e\n\u003ctd\u003ex2\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003e2WD Chassis\u003c\/td\u003e\n\u003ctd\u003ex1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eCastor Wheel\u003c\/td\u003e\n\u003ctd\u003ex1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003e18650 Cell\u003c\/td\u003e\n\u003ctd\u003ex2\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003e18650 Holder\u003c\/td\u003e\n\u003ctd\u003ex1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eUSB Cable\u003c\/td\u003e\n\u003ctd\u003ex1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eM-F Wires\u003c\/td\u003e\n\u003ctd\u003ex20\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eM-M Wires\u003c\/td\u003e\n\u003ctd\u003ex10\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\u003eDesigned for CBSE Class 11–12 students tackling AI\/ML practicals and B.Tech ECE\/EEE first‑years exploring reinforcement learning. ATL Tinkering Lab members and Smart India Hackathon participants can prototype an adaptive robot quickly. IIT, NIT, VIT, and BITS freshers also use this kit as a hands-on first step into embedded AI.\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\u003eOur AI companion walks you through wiring and code uploads, and you can send a WhatsApp message for human help within hours.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eHow does the Q-learning algorithm fit on an Arduino with such limited memory?\u003c\/summary\u003e\u003cp\u003eThe sensor states are discretised into a compact Q-table of only a few hundred entries, stored in EEPROM so learning persists across power cycles.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eCan I use a custom track shape, or is it tied to one layout?\u003c\/summary\u003e\u003cp\u003eThe agent learns any black-line-on-white pattern. You can change the track between runs; it adapts its policy to the new geometry.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eWhat if I want to train for more than 20 laps?\u003c\/summary\u003e\u003cp\u003eThe default sketch includes a configurable lap counter. You can increase it to see if the policy refines further, or lower it for faster sessions.\u003c\/p\u003e\u003c\/details\u003e\n\n\u003cdiv class=\"kit-description\"\u003e\n  \u003cp\u003eQ-learning agent on Arduino replaces hard-coded PID — learns optimal motor response from IR sensor states over 20 laps.\u003c\/p\u003e\n  \u003ch4\u003eWhat's in this kit\u003c\/h4\u003e\n  \u003cul\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/uno-r3-ch340g-atmega328p-board-arduino-compatible\"\u003eArduino Uno R3\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\n\u003ca href=\"\/products\/tcrt5000-ir-sensor-module-for-obstacle-avoidance-line-following-0342\"\u003eTCRT5000 IR Module\u003c\/a\u003e x3\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/l298n-motor-driver-module-specs-pinout-projects-compoden\"\u003eL298N Motor Driver\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\n\u003ca href=\"\/products\/tt-gear-motor-high-torque-dc-motor-for-arduino-robotics\"\u003eTT Gear Motor\u003c\/a\u003e x2\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/2wd-robot-chassis-kit-with-motors-and-wheels-for-diy-robotics\"\u003e2WD Chassis\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/castor-wheel-15-inch-nylon-robot-chassis-mobility\"\u003eCastor Wheel\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\n\u003ca href=\"\/products\/18650-cell-37v-2600mah-li-ion-battery\"\u003e18650 Cell\u003c\/a\u003e x2\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/18650-battery-holder-single-cell-cylindrical-holder-for-diy-electronics\"\u003e18650 Holder\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/esp32-cam-mb-programmer-module-with-micro-usb-ch340g-plug-play\"\u003eUSB Cable\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003eM-F Wires x20\u003c\/li\u003e\n    \u003cli\u003eM-M Wires x10\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 Arduino Q-Learning Line Follower Kit with Arduino Uno + Motor?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"The Arduino Q-Learning Line Follower Kit with Arduino Uno + Motor includes all components needed: Arduino Uno R3, TCRT5000 IR Module, L298N Motor Driver, TT Gear Motor, 2WD Chassis 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 Arduino Q-Learning Line Follower Kit with Arduino Uno + Motor?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"This kit is designed for Beginner level makers, suitable for ages 15-18. Q-learning agent on Arduino replaces hard-coded PID — learns optimal motor response from IR sensor states over 20 laps. Estimated build time is 3-4 hrs.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I buy the Arduino Q-Learning Line Follower Kit with Arduino Uno + Motor online in India?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes, the Arduino Q-Learning Line Follower Kit with Arduino Uno + Motor is available online at Compoden (compoden.in), India's AI-powered electronics and robotics store. Ships from Bengaluru in 1-5 business days across India.\"\n      }\n    }\n  ]\n}\n\u003c\/script\u003e\n\n\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"Product\",\n  \"name\": \"Arduino Q-Learning Line Follower Kit with Arduino Uno + Motor\",\n  \"description\": \"Q-learning agent on Arduino replaces hard-coded PID — learns optimal motor response from IR sensor states over 20 laps.\",\n  \"sku\": \"CDN-KIT-2401\",\n  \"brand\": {\"@type\": \"Brand\", \"name\": \"Compoden\"},\n  \"offers\": {\n    \"@type\": \"Offer\",\n    \"url\": \"https:\/\/compoden.in\/products\/kit-arduino-q-learning-line-follower\",\n    \"priceCurrency\": \"INR\",\n    \"price\": \"2100\",\n    \"availability\": \"https:\/\/schema.org\/InStock\",\n    \"seller\": {\"@type\": \"Organization\", \"name\": \"Compoden\"}\n  },\n  \"category\": \"AI Robotics\"\n}\n\u003c\/script\u003e","brand":"Compoden","offers":[{"title":"Clone","offer_id":53469359407469,"sku":"CDN-KIT-2401-CL","price":2480.0,"currency_code":"INR","in_stock":true},{"title":"R3 Original","offer_id":53469359440237,"sku":"CDN-KIT-2401-R3","price":4750.0,"currency_code":"INR","in_stock":true},{"title":"R4 WiFi","offer_id":53469359473005,"sku":"CDN-KIT-2401-R4","price":4110.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0999\/3997\/5533\/files\/kit-arduino-q-learning-line-follower.png?v=1781948205","url":"https:\/\/compoden.com\/products\/kit-arduino-q-learning-line-follower","provider":"Compoden","version":"1.0","type":"link"}