This paper proposes using reinforcement learning (RL) agents enhanced with 3D vision-language models (VLMs) to enable sustainable smart homes. These agents perceive the 3D layout and objects of a household environment and learn to autonomously adjust systems (e.g., HVAC, lighting, appliances) to optimize energy use and resource management. We identify specific energysaving tasks (such as occupancy-driven thermostat control, efficient lighting and blind management) and resource-management tasks (like waste sorting assistance and water-use feedback) that such agents can perform. We review recent advances in RL and vision-language models, and outline a conceptual framework for embodied home agents. Through this synthesis, we demonstrate how RL-powered agents can significantly reduce domestic energy consumption and waste, thereby supporting eco-friendly lifestyles. We also discuss the potential environmental and economic benefits of these systems, as well as technical and social challenges to their adoption. The contribution of this work is in articulating “spatial adaptation” for sustainability: an RL-driven approach that transforms smart homes into proactive, learning environments for green living.