用1比特大模型+强化学习，在树莓派上实现省电又智能的灯光控制。
BitRL-Light: 1-bit LLM Agents with Deep Reinforcement Learning for Energy-Efficient Smart Home Lighting Optimization
- 用1比特量化LLM结合DQN强化学习，实现在边缘设备上的实时决策。
- 比传统规则系统节能32%，在树莓派上延迟低于200ms，用户满意度达95%。
- 适合资源受限的智能家居场景，无需云端支持，可处理自然语言指令。
智能家居照明占住宅能耗的15-20%，但缺乏同时兼顾用户舒适与能效的自适应智能。本文提出BitRL-Light框架，将1比特量化的大语言模型（LLM）与深度Q网络（DQN）强化学习结合，实现边缘设备上的实时照明控制。系统在Raspberry Pi上部署1比特量化Llama-3.2-1B模型，相比全精度模型能耗降低71.4倍，仍保持智能控制能力。通过多目标强化学习，系统基于用户反馈学习最优照明策略，平衡能耗、舒适度与昼夜节律。实验表明，相较规则系统节能32%，在Raspberry Pi 4上推理延迟低于200ms，用户满意度达95%。系统支持通过Google Home/IFTTT接收自然语言指令，并通过手动覆盖获取隐式反馈。对比分析显示，1比特模型在ARM处理器上比2比特模型快5.07倍，任务准确率仍保持92%。本工作为资源受限物联网设备部署自适应AI提供了实用方案，实现无云依赖的智能家居自动化。
原文摘要 · Abstract (English)
Smart home lighting systems consume 15-20% of residential energy but lack adaptive intelligence to optimize for user comfort and energy efficiency simultaneously. We present BitRL-Light, a novel framework combining 1-bit quantized Large Language Models (LLMs) with Deep Q-Network (DQN) reinforcement learning for real-time smart home lighting control on edge devices. Our approach deploys a 1-bit quantized Llama-3.2-1B model on Raspberry Pi hardware, achieving 71.4 times energy reduction compared to full-precision models while maintaining intelligent control capabilities. Through multi-objective reinforcement learning, BitRL-Light learns optimal lighting policies from user feedback, balancing energy consumption, comfort, and circadian alignment. Experimental results demonstrate 32% energy savings compared to rule-based systems, with inference latency under 200ms on Raspberry Pi 4 and 95% user satisfaction. The system processes natural language commands via Google Home/IFTTT integration and learns from implicit feedback through manual overrides. Our comparative analysis shows 1-bit models achieve 5.07 times speedup over 2-bit alternatives on ARM processors while maintaining 92% task accuracy. This work establishes a practical framework for deploying adaptive AI on resource-constrained IoT devices, enabling intelligent home automation without cloud dependencies.
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