BitRL-Light: 1-bit LLM Agents with Deep Reinforcement Learning for Energy-Efficient Smart Home Lighting Optimization

Smart home lighting systems consume 15-20% of residential energy but often lack adaptive intelligence. BitRL-Light is a novel framework that combines 1-bit quantized Large Language Models (LLMs) with Deep Q-Network (DQN) reinforcement learning for real-time, energy-efficient smart home lighting control on edge devices. It deploys a 1-bit quantized Llama-3.2-1B model on Raspberry Pi hardware, achieving a 71.4 times energy reduction compared to full-precision models while maintaining intelligent control capabilities. Experimental results show 32% energy savings over rule-based systems, inference latency under 200ms on Raspberry Pi 4, and 95% user satisfaction. The system integrates with Google Home/IFTTT and learns from implicit feedback, providing a practical framework for adaptive AI on resource-constrained IoT devices without cloud dependencies.