mlsanityData to 7 Oct 2026

Multi-Agent Reinforcement Learning for Fast-Timescale Demand Response of Residential Loads

arXiv 2301.02593 ↗First posted 6 Jan 2023cs.MA cs.AI cs.LGcited by 2

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Abstract (arXiv)

To integrate high amounts of renewable energy resources, electrical power grids must be able to cope with high amplitude, fast timescale variations in power generation. Frequency regulation through demand response has the potential to coordinate temporally flexible loads, such as air conditioners, to counteract these variations. Existing approaches for discrete control with dynamic constraints struggle to provide satisfactory performance for fast timescale action selection with hundreds of agents. We propose a decentralized agent trained with multi-agent proximal policy optimization with localized communication. We explore two communication frameworks: hand-engineered, or learned through targeted multi-agent communication. The resulting policies perform well and robustly for frequency regulation, and scale seamlessly to arbitrary numbers of houses for constant processing times.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 26 of its 71 references that are in the map. First posted on arXiv 6 Jan 2023.

Builds on

26 of 71 references.

References · 26

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2017Proximal Policy Optimization Algorithmscited by 14,441
  3. 2015Continuous control with deep reinforcement learningcited by 3,279
  4. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  5. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
  6. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,084
  7. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 903
  8. 2016CVXPY: A Python-Embedded Modeling Language for Convex Optimizationcited by 633
  9. 2019Quantifying the Carbon Emissions of Machine Learningcited by 516
  10. 2019Emergent Tool Use From Multi-Agent Autocurriculacited by 308
  11. 2018Mean Field Multi-Agent Reinforcement Learningcited by 202
  12. 2018TarMAC: Targeted Multi-Agent Communicationcited by 201
14 more
  1. 2018Learning Attentional Communication for Multi-Agent Cooperationcited by 170
  2. 2019CoLight: Learning Network-level Cooperation for Traffic Signal Controlcited by 133
  3. 2017Time Limits in Reinforcement Learningcited by 72
  4. 2021Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networkscited by 52
  5. 2021Reinforcement Learning for Ridesharing: An Extended Surveycited by 35
  6. 2020CityLearn: Standardizing Research in Multi-Agent Reinforcement Learning for Demand Response and Urban Energy Managementcited by 27
  7. 2021PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systemscited by 15
  8. 2021GridLearn: Multiagent Reinforcement Learning for Grid-Aware Building Energy Managementcited by 12
  9. 2021Theory of Mind for Deep Reinforcement Learning in Hanabicited by 11
  10. 2020COHORT: Coordination of Heterogeneous Thermostatically Controlled Loads for Demand Flexibilitycited by 5
  11. 2015Power Systems Without Fuelcited by 4
  12. 2017Online Stochastic Optimization of Networked Distributed Energy Resourcescited by 4
  13. 2020Online Convex Optimization with Binary Constraintscited by 4
  14. 2017Setpoint Tracking with Partially Observed Loadscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 0; 2026: 1.

2 papers.

  1. 2023Dealing With Non-stationarity in Decentralized Cooperative Multi-Agent Deep Reinforcement Learning via Multi-Timescale Learningcited by 12
  2. 2026Scalable Constrained Multi-Agent Reinforcement Learning via State Augmentation and Consensus for Separable Dynamicscited by 1