Near-optimal Deep Reinforcement Learning Policies from Data for Zone Temperature Control
This addresses the energy efficiency problem in the building sector by providing evidence for the effectiveness of DRL controllers, though it is incremental as it builds on existing DRL methods with a new evaluation approach.
The paper tackles the problem of evaluating Deep Reinforcement Learning (DRL) agents for zone temperature control in buildings by comparing them to theoretically optimal solutions, using Physically Consistent Neural Networks (PCNNs) as simulation environments, and finds that DRL agents outperform conventional controllers and achieve near-optimal performance.
Replacing poorly performing existing controllers with smarter solutions will decrease the energy intensity of the building sector. Recently, controllers based on Deep Reinforcement Learning (DRL) have been shown to be more effective than conventional baselines. However, since the optimal solution is usually unknown, it is still unclear if DRL agents are attaining near-optimal performance in general or if there is still a large gap to bridge. In this paper, we investigate the performance of DRL agents compared to the theoretically optimal solution. To that end, we leverage Physically Consistent Neural Networks (PCNNs) as simulation environments, for which optimal control inputs are easy to compute. Furthermore, PCNNs solely rely on data to be trained, avoiding the difficult physics-based modeling phase, while retaining physical consistency. Our results hint that DRL agents not only clearly outperform conventional rule-based controllers, they furthermore attain near-optimal performance.