mlsanityData to 14 Oct 2026

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

arXiv 2608.11324 ↗First posted 11 Aug 2026cs.LG cs.AI cs.NEcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 16 of its 26 references that are in the map. First posted on arXiv 11 Aug 2026.

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16 of 26 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024CityLearn v2: Energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities2 May

Other references · 15

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2018Evolution-Guided Policy Gradient in Reinforcement Learningcited by 83
  3. 2018Discovering the Elite Hypervolume by Leveraging Interspecies Correlationcited by 41
  4. 2019Collaborative Evolutionary Reinforcement Learningcited by 37
  5. 2024Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid Algorithmscited by 26
  6. 2021Physics Informed Neural Networks for Control Oriented Thermal Modeling of Buildingscited by 24
  7. 2020Quality Diversity for Multi-task Optimizationcited by 23
  8. 2022Combining Evolution and Deep Reinforcement Learning for Policy Search: a Surveycited by 23
  9. 2023QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Accelerationcited by 17
  10. 2020Fast and stable MAP-Elites in noisy domains using deep gridscited by 13
  11. 2024Quality-Diversity Actor-Critic: Learning High-Performing and Diverse Behaviors via Value and Successor Features Criticscited by 13
  12. 2023Multi-zone HVAC Control with Model-Based Deep Reinforcement Learningcited by 6
3 more
  1. 2024Parametric-Task MAP-Elitescited by 4
  2. 2025Data Center Cooling System Optimization Using Offline Reinforcement Learningcited by 4
  3. 2026Data Center Chiller Plant Optimization via Mixed-Integer Nonlinear Differentiable Predictive Controlcited by 2

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  3. 2025A Receding Horizon Reinforcement Learning Framework for Campus Chiller Energy Management - A case study from an Australian University
  4. 2025Towards Machine Learning-based Model Predictive Control for HVAC Control in Multi-Context Buildings at Scale via Ensemble Learning
  5. 2025BEAVER: Building Environments with Assessable Variation for Evaluating Multi-Objective Reinforcement Learning
  6. 2026Reinforcement Learning-Based Co-Design and Operation of Chiller and Thermal Energy Storage for Cost-Optimal HVAC Systems
  7. 2026Hierarchical Control Framework Integrating LLMs with RL for Decarbonized HVAC Operation
  8. 2024Go Beyond Black-box Policies: Rethinking the Design of Learning Agent for Interpretable and Verifiable HVAC Control