Contextual Quality-Diversity Evolutionary Reinforcement Learning for HVAC Control in Tropical Commercial Buildings

Researchers propose CQD-ERL, a contextual quality-diversity evolutionary reinforcement-learning controller for HVAC control in tropical commercial buildings, which maintains a product archive of specialized policies indexed by operating context and behavior descriptor, filtered through a deterministic safety shield. Trained on a reduced-order environment representing a Singapore commercial building, it is evaluated over a full annual backtest against an ASHRAE Guideline 36 baseline.

arXiv:2608.11324v1 Announce Type: new Abstract: This paper proposes a contextual quality-diversity evolutionary reinforcement-learning controller, CQD-ERL, for the supervisory control of a tropical, water-cooled chiller plant and its associated air side. Rather than converging to a single scalarised policy, the controller maintains a product archive of specialised policies indexed jointly by a data- driven operating context, a cluster of daily weather and load regime, and a context-invariant behaviour descriptor, filled by a gradient-free evolutionary operator and a soft-actor-critic policy-gradient operator that share one replay buffer. Every action is filtered through a deterministic safety shield before execution. The controller is trained on a two-tier reduced-order environment representing the latent load, cooling-tower approach and humidity constraints of a Singapore commercial building, and is evaluated over a full annual backtest against an ASHRAE Guideline 36 baseline.