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CityLearn: Standardizing Research in Multi-Agent Reinforcement Learning for Demand Response and Urban Energy Management

arXiv 2012.10504 ↗First posted 18 Dec 2020cs.LG cs.AIcited by 27

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Abstract (arXiv)

Rapid urbanization, increasing integration of distributed renewable energy resources, energy storage, and electric vehicles introduce new challenges for the power grid. In the US, buildings represent about 70% of the total electricity demand and demand response has the potential for reducing peaks of electricity by about 20%. Unlocking this potential requires control systems that operate on distributed systems, ideally data-driven and model-free. For this, reinforcement learning (RL) algorithms have gained increased interest in the past years. However, research in RL for demand response has been lacking the level of standardization that propelled the enormous progress in RL research in the computer science community. To remedy this, we created CityLearn, an OpenAI Gym Environment which allows researchers to implement, share, replicate, and compare their implementations of RL for demand response. Here, we discuss this environment and The CityLearn Challenge, a RL competition we organized to propel further progress in this field.

Key facts. Cited by 27 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Dec 2020.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 3; 2022: 5; 2023: 4; 2024: 7; 2025: 2; 2026: 6.

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  1. 2024Application-Driven Innovation in Machine Learningcited by 24
  2. 2021PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systemscited by 16
  3. 2021GridLearn: Multiagent Reinforcement Learning for Grid-Aware Building Energy Managementcited by 12
  4. 2021Real-world challenges for multi-agent reinforcement learning in grid-interactive buildingscited by 9
  5. 2025RL2Grid: Benchmarking Reinforcement Learning in Power Grid Operationscited by 9
  6. 2023Off-the-Grid MARL: Datasets with Baselines for Offline Multi-Agent Reinforcement Learningcited by 8
  7. 2022Winning the CityLearn Challenge: Adaptive Optimization with Evolutionary Search under Trajectory-based Guidancecited by 7
  8. 2023Federated Ensemble-Directed Offline Reinforcement Learningcited by 6
  9. 2022Performance Comparison of Deep RL Algorithms for Energy Systems Optimal Schedulingcited by 4
  10. 2022MERLIN: Multi-agent offline and transfer learning for occupant-centric energy flexible operation of grid-interactive communities using smart meter data and CityLearncited by 4
  11. 2024CommonPower: A Framework for Safe Data-Driven Smart Grid Controlcited by 4
  12. 2024RL-ADN: A High-Performance Deep Reinforcement Learning Environment for Optimal Energy Storage Systems Dispatch in Active Distribution Networkscited by 4
15 more
  1. 2024EnergAIze: Multi Agent Deep Deterministic Policy Gradient for Vehicle to Grid Energy Managementcited by 3
  2. 2023Multi-Agent Reinforcement Learning for Fast-Timescale Demand Response of Residential Loadscited by 2
  3. 2024Impact of data for forecasting on performance of model predictive control in buildings with smart energy storagecited by 2
  4. 2022B2RL: An open-source Dataset for Building Batch Reinforcement Learningcited by 1
  5. 2022BEAR: Physics-Principled Building Environment for Control and Reinforcement Learningcited by 1
  6. 2026Scalable Constrained Multi-Agent Reinforcement Learning via State Augmentation and Consensus for Separable Dynamicscited by 1
  7. 2023Adaptive Control of Resource Flow to Optimize Construction Work and Cash Flow via Online Deep Reinforcement Learningcited by 0
  8. 2024A Novel Bifurcation Method for Observation Perturbation Attacks on Reinforcement Learning Agents: Load Altering Attacks on a Cyber Physical Power Systemcited by 0
  9. 2024Optimization Solution Functions as Deterministic Policies for Offline Reinforcement Learningcited by 0
  10. 2025BuildingGym: An open-source toolbox for AI-based building energy management using reinforcement learningcited by 0
  11. 2026MARLEM: A Multi-Agent Reinforcement Learning Simulation Framework for Implicit Cooperation in Decentralized Local Energy Marketscited by 0
  12. 2026Characterizing MARL for Energy Control: A Multi-KPI Benchmark on the CityLearn Environmentcited by 0
  13. 2026From Pixels to Digital Agents: An Empirical Study on the Taxonomy and Technological Trends of Reinforcement Learning Environmentscited by 0
  14. 2026PIRS: Physics-Informed Reward Shaping for SAC-Based Building Energy Managementcited by 0
  15. 2026CityLearn v3: A Configurable Simulation and Evaluation Framework for Realistic Control Studies of Renewable Energy Communitiescited by 0