Baby Room Comfort Monitor Kit v24 – Raspberry Pi 5-Powered MARL HVAC Optimisation

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

Transform a standard baby nursery into an autonomously optimised environment. This advanced kit lets you deploy multiple reinforcement learning agents on a single Raspberry Pi 5, each managing a separate zone’s temperature and humidity via ESP32-DHT22 nodes. The agents constantly negotiate a shared energy budget—deciding which zone to heat, cool, or hold back—mimicking real-world multi‑agent resource allocation. Originally prototyped for infant care settings where consistent comfort matters most, the architecture generalises to any building‑wide HVAC challenge.

What You'll Build

You’ll assemble a distributed sensor‑actuator network across five zones. Each ESP32 reads local DHT22 data and reports to the Pi 5, which runs five independent RL agents. The agents learn policies that maintain target temperature and humidity ranges while collectively respecting the energy ceiling you set. A dashboard shows real‑time zone status, energy draw, and agent decisions, proving that MARL can outperform naïve thermostat rules even under tight constraints.

What You'll Learn

• How to implement multi-agent reinforcement learning (MARL) with independent Q-learners on a resource‑constrained edge device
• Coordinating wireless sensor networks using ESP32, MQTT, and DHT22 with sub‑second latency
• Designing and coding an energy‑budget negotiation protocol that avoids zone starvation
• Integrating 5 V relay modules for safe, isolated control of fans, heaters, or humidifiers
• Profiling and tuning RL inference on Raspberry Pi 5 to keep update cycles under 500 ms even with multiple agents

Kit Contents

Why Buy This Kit Instead of Sourcing Parts Separately

Who This Kit Is For

Ideal for B.Tech ECE/EEE final‑year students working on intelligent building systems, participants in Smart India Hackathon tackling HVAC challenges, and IIT/NIT/VIT learners specialising in reinforcement learning. The kit suits anyone who has already built a basic IoT project and wants to push into multi-agent AI on real hardware, not just simulations.

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.