ESP32 Q-Learning Thermostat Kit - Build AI IoT Project
Build an AI-Driven IoT System That Learns Your Comfort Schedule
Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.
Imagine an ESP32 that figures out when to warm your room - not because you programmed a schedule, but because it learned from your actual presence and the changing weather. This kit turns that idea into a working device: a Q-learning agent running directly on an ESP32, making heating decisions from PIR occupancy data and DHT22 temperature/humidity readings. No cloud, no predefined rules, just adaptive intelligence at the edge.
What You'll Build
You'll assemble a self-contained thermostat that monitors a room with a PIR motion sensor and a precision DHT22, keeps time with a DS3231 RTC, and controls a heater (or any 5V-switched appliance) via a relay. The 0.96in OLED displays the learning progress, current state, and chosen action. After a few hours of exploration, the ESP32's Q-learning policy will start anticipating occupancy patterns - turning the heater on before you arrive and off when the room is empty or already warm enough, learning continuously as seasons and routines shift.
What You'll Learn
- Implement a complete Q-learning algorithm on a resource-constrained ESP32 microcontroller
- Interface DHT22, HC-SR501 PIR, DS3231 RTC, OLED, and a relay module to form a single IoT system
- Design state spaces that encode occupancy, temperature, and time for reinforcement learning
- Visualize and debug learning progress directly on the OLED screen and serial output
Kit Contents
| Component | Quantity |
|---|---|
| ESP32 Dev Board | 1 |
| DHT22 | 1 |
| HC-SR501 PIR | 1 |
| DS3231 RTC | 1 |
| 5V Relay Module | 1 |
| 0.96in OLED | 1 |
| MicroUSB Cable | 1 |
| M-M Wires | 20 |
Why Buy This Kit Instead of Sourcing Parts Separately
| Factor | Sourcing Separately | Compoden Kit |
|---|---|---|
| Compatibility checks | You verify every part | Pre-tested as a system |
| Build support | Forums and scattered tutorials | AI companion trained on this exact project |
| Time to first working build | Days of debugging | Hours, with step-by-step guidance |
| Shipping coordination | Multiple sellers, multiple delays | One shipment from Bengaluru in 3-5 days |
Who This Kit Is For
Designed for intermediate makers aged 15-21, this project fits seamlessly into CBSE Class 11-12 AI practicals, B.Tech ECE/EEE mini-projects, and Smart India Hackathon prototypes. ATL Tinkering Lab mentors can use it to demonstrate true edge intelligence, while IIT, NIT, VIT, and BITS students get a ready-to-run reinforcement learning platform for IoT courses.
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.
What if I get stuck during the build?
Scan the QR code on the box to chat with the AI companion trained on this exact project. It will guide you step by step, and if you still need help, message us on WhatsApp for human support.
Does the ESP32 run the Q-learning algorithm locally?
Yes, the entire reinforcement learning loop - state observation, action selection, reward computation, and Q-table update - executes on the ESP32. No cloud connection is needed for learning or decision-making after the code is uploaded.
Can I control a real heater with this kit?
The included 5V relay module can switch a low-power DC heater or a solid-state relay for AC loads. For mains-powered heaters, always use an electrician and ensure proper isolation. The kit teaches the control logic; actual deployment must follow local electrical safety norms.
What prior knowledge do I need?
You should be comfortable with Arduino IDE and basic C/C++. Familiarity with Python is a plus for understanding the algorithm, but the companion explains the Q-learning concepts from scratch. Complete beginners may find the AI guidance sufficient with some extra time.
Q-learning agent on ESP32 learns optimal heating schedule from occupancy and weather patterns - no pre-programmed rules.
What's in this kit
Shipping Information
- Prepaid Orders: ₹75 for orders up to ₹999, FREE shipping above ₹999
- COD Orders: ₹125 shipping + ₹50 COD fee = ₹175 total
- Delivery Timeline: Dispatch in 1-2 days, delivery in 2-7 days depending on location
Returns & Warranty
- 7-Day Return: Manufacturing defects only (approval required)
- Warranty: 7 days from delivery
- Non-Returnable: Batteries, consumables, cut wires, clearance items