mlsanityData to 14 Oct 2026

BuildingGym: An open-source toolbox for AI-based building energy management using reinforcement learning

arXiv 2509.11922 ↗First posted 15 Sep 2025cs.AIcited by 0

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

Abstract (arXiv)

Reinforcement learning (RL) has proven effective for AI-based building energy management. However, there is a lack of flexible framework to implement RL across various control problems in building energy management. To address this gap, we propose BuildingGym, an open-source tool designed as a research-friendly and flexible framework for training RL control strategies for common challenges in building energy management. BuildingGym integrates EnergyPlus as its core simulator, making it suitable for both system-level and room-level control. Additionally, BuildingGym is able to accept external signals as control inputs instead of taking the building as a stand-alone entity. This feature makes BuildingGym applicable for more flexible environments, e.g. smart grid and EVs community. The tool provides several built-in RL algorithms for control strategy training, simplifying the process for building managers to obtain optimal control strategies. Users can achieve this by following a few straightforward steps to configure BuildingGym for optimization control for common problems in the building energy management field. Moreover, AI specialists can easily implement and test state-of-the-art control algorithms within the platform. BuildingGym bridges the gap between building managers and AI specialists by allowing for the easy configuration and replacement of RL algorithms, simulators, and control environments or problems. With BuildingGym, we efficiently set up training tasks for cooling load management, targeting both constant and dynamic cooling load management. The built-in algorithms demonstrated strong performance across both tasks, highlighting the effectiveness of BuildingGym in optimizing cooling strategies.

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 4 of its 43 references that are in the map. First posted on arXiv 15 Sep 2025.

Builds on

4 of 43 references.

References · 4

  1. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  2. 2017A Brief Survey of Deep Reinforcement Learningcited by 483
  3. 2017On-line Building Energy Optimization using Deep Reinforcement Learningcited by 27
  4. 2020CityLearn: Standardizing Research in Multi-Agent Reinforcement Learning for Demand Response and Urban Energy Managementcited by 27

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  2. 2025BEAVER: Building Environments with Assessable Variation for Evaluating Multi-Objective Reinforcement Learning
  3. 2025Data-Driven Policy Mapping for Safe RL-based Energy Management Systems
  4. 2026Building2Building: A Large Scale Benchmark for Generalizable Real-World Reinforcement Learning
  5. 2026AutoB2G: Agentic Simulation and Reinforcement Learning for Spatio-Temporal Grid-Interactive Building Control
  6. 2021Generalized Reinforcement Learning for Building Control using Behavioral Cloning
  7. 2026Explainable Data-driven Deep Reinforcement Learning Methods for Optimal Energy Management in Buildings
  8. 2026Counter-Dyna: Data-Efficient RL-Based HVAC Control using Counterfactual Building Models