A Hybrid Classifier-Guided Deep Q-Learning Approach for Multi-Objective Building Environment Management

Abstract

A comfort-aware reinforcement learning (RL) framework is presented for intelligent indoor environment control in smart buildings. The system comprises three primary components: a data-driven comfort classifier, a lightweight indoor environment simulator, and a Deep Q-Network (DQN) control agent. A synthetic dataset, representing typical thermal and air-quality conditions, is pre-processed and used to train a Random Forest model that classifies occupant comfort in real time and translates these predictions into reward signals for the RL agent. The agent undergoes initial offline pre-training using a replay buffer populated with synthetic state–action–reward transitions, followed by further refinement through online interaction with the simulator to enhance sample efficiency and stability. Control performance is evaluated against a simple rule-based baseline over 100 episodes with varying internal gains and weather scenarios. The comfort classifier achieves high accuracy across all comfort categories, supporting reliable and consistent reward generation. As a result, the RL controller attains significantly higher comfort levels than the baseline while maintaining comparable or lower energy consumption. Pareto analysis indicates that the RL strategy consistently produces superior comfort–energy trade-offs, with most Pareto-optimal operating points attributed to the DQN agent. These results highlight the potential of RL as an adaptive, data-driven approach for multi-objective indoor environmental control in intelligent buildings.