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

• URL: http://arxiv.org/abs/2608.11324v1
• Date: Tue, 11 Aug 2026 18:16:59 GMT
• Title: Contextual Quality-Diversity Evolutionary Reinforcement Learning for HVAC Control in Tropical Commercial Buildings
• Abstract summary: This paper proposes a contextual quality-diversity evolutionary reinforcement-learning controller, CQD-ERL.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• A Unified Python Framework for Direct PPO-based Control of AHUs with Economizer Logic and CO2-Constrained Ventilation [0.0]
  This paper presents a new approach to optimizing HVAC systems through the use of Deep Reinforcement Learning (DRL) algorithms.<n>The DRL system uses a second order-capacitor thermal model and an integrated dynamic mass balance of CO2 to replicate the complex physics associated with buildings.<n>An enthalpy-based economiser is used to create free cooling from the outdoor environment.
  arXiv  Detail & Related papers  (2026-05-23T05:31:09Z)
• MoASE++: Mixture of Activation Sparsity Experts with Domain-Adaptive On-policy Distillation for Continual Test Time Adaptation [65.068801413044]
  Continual test-time adaptation adapts a source-pretrained model to non-stationary, unlabeled target streams.<n>We introduce MoASE, a mixture-of-experts that disentangles domain-agnostic structure from domain-specific texture.
  arXiv  Detail & Related papers  (2026-05-18T01:52:12Z)
• Closed-Loop CO2 Storage Control With History-Based Reinforcement Learning and Latent Model-Based Adaptation [0.0]
  This work formulates CO2 injection and brine-production control as a partially observable sequential decision problem.<n>We first compare privileged-state, well-only, history-conditioned, masking-curriculum, and asymmetric teacher-student model-free policies.<n>We then evaluate a latent model-based adaptation pipeline that reuses nominal latent dynamics and retunes controllers under known injector failure.
  arXiv  Detail & Related papers  (2026-05-04T09:49:44Z)
• AR-CoPO: Align Autoregressive Video Generation with Contrastive Policy Optimization [47.39648302477585]
  Streaming autoregressive (AR) video generators combined with few-step distillation achieve low-latency, high-quality synthesis.<n>We propose AR-CoPO, a framework that adapts the Neighbor GRPO contrastive perspective to streaming AR generation.
  arXiv  Detail & Related papers  (2026-03-18T08:07:01Z)
• Diffusion Controller: Framework, Algorithms and Parameterization [54.82539154511621]
  We present a unified control-theoretic view that casts reverse diffusion sampling as state-only control within (generalized) linearly-solvable Markov Decision Processes.<n>Under this framework, control acts by reweighting the pretrained reverse-time transition kernels, balancing terminal objectives against an $f$-divergence cost.<n>Experiments on Stable Diffusion v1.4 show consistent gains in preference-alignment win rates and improved quality-efficiency trade-offs.
  arXiv  Detail & Related papers  (2026-03-07T01:49:59Z)
• Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control [61.155940786140455]
  Reinforcement learning (RL) has shown promising results in active flow control (AFC)<n>Current AFC benchmarks rely on external computational fluid dynamics (CFD) solvers, are not fully differentiable, and provide limited 3D and multi-agent support.<n>We introduce FluidGym, the first standalone, fully differentiable benchmark suite for RL in AFC.
  arXiv  Detail & Related papers  (2026-01-21T14:13:44Z)
• Shielded Controller Units for RL with Operational Constraints Applied to Remote Microgrids [50.64533198075622]
  Reinforcement learning (RL) is a powerful framework for optimizing decision-making in complex systems under uncertainty.<n>In this paper, we introduce Shielded Controller Units (SCUs), a systematic and interpretable approach that leverages prior knowledge of system dynamics.<n>We demonstrate the effectiveness of SCUs on a remote microgrid optimization task with strict operational requirements.
  arXiv  Detail & Related papers  (2025-11-30T19:28:34Z)
• ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning [46.67828766038463]
  We propose a framework, Adaptive Conservative Level in Q-Learning (ACL-QL), which limits the Q-values in a mild range.<n>ACL-QL enables adaptive control on the conservative level over each state-action pair, i.e., lifting the Q-values more for good transitions and less for bad transitions.<n>Motivated by the theoretical analysis, we propose a novel algorithm, ACL-QL, which uses two learnable adaptive weight functions to control the conservative level over each transition.
  arXiv  Detail & Related papers  (2024-12-22T04:18:02Z)
• Dichotomy of Control: Separating What You Can Control from What You Cannot [129.62135987416164]
  We propose a future-conditioned supervised learning framework that separates mechanisms within a policy's control (actions) from those beyond a policy's control (environmentity) We show that DoC yields policies that are consistent with their conditioning inputs, ensuring that conditioning a learned policy on a desired high-return future outcome will correctly induce high-return behavior.
  arXiv  Detail & Related papers  (2022-10-24T17:49:56Z)
• Robust Action Governor for Uncertain Piecewise Affine Systems with Non-convex Constraints and Safe Reinforcement Learning [5.7406123763046955]
  The action governor is an add-on scheme to a nominal control loop that monitors and adjusts the control actions to enforce safety specifications expressed as point-in-time state and constraints. We introduce the Robust Action Governor (RAG) of systems which can be represented as non-time additive uncertainties. This development enables safe real-time evolution of the environment and adaptation to changes in system parameters.
  arXiv  Detail & Related papers  (2022-07-17T17:31:07Z)
• Development of a Soft Actor Critic Deep Reinforcement Learning Approach for Harnessing Energy Flexibility in a Large Office Building [0.0]
  This research is concerned with the novel application and investigation of Soft Actor Critic' (SAC) based Deep Reinforcement Learning (DRL) SAC is a model-free DRL technique that is able to handle continuous action spaces.
  arXiv  Detail & Related papers  (2021-04-25T10:33:35Z)
• Online Learning and Distributed Control for Residential Demand Response [16.61679791774638]
  This paper studies the automated control method for regulating air conditioner (AC) loads in incentive-based residential demand response (DR) We formulate the AC control problem in a DR event as a multi-period transition optimization that integrates the indoor thermal dynamics and customer opt-out status. We propose an online DR control algorithm to learn customer behaviors and make real-time AC control schemes.
  arXiv  Detail & Related papers  (2020-10-11T03:52:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.