# MARL-iDR: Multi-Agent Reinforcement Learning for Incentive-based Residential Demand Response

arXiv: https://arxiv.org/abs/2304.04086 · HTML: https://mlsanity.com/paper/2304.04086/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2023.

## Built on by (most cited first, 4 of 4)

- [Learning a Reward Function for User-Preferred Appliance Scheduling](https://mlsanity.com/paper/2310.07389/) (2023)
- [MARLEM: A Multi-Agent Reinforcement Learning Simulation Framework for Implicit Cooperation in Decentralized Local Energy Markets](https://mlsanity.com/paper/2602.16063/) (2026)
- [Capacity-constrained demand response in smart grids using deep reinforcement learning](https://mlsanity.com/paper/2602.16525/) (2026)
- [Towards Affordable Energy: A Gymnasium Environment for Electric Utility Demand-Response Programs](https://mlsanity.com/paper/2605.12462/) (2026)

## Abstract (arXiv)

This paper presents a decentralized Multi-Agent Reinforcement Learning (MARL) approach to an incentive-based Demand Response (DR) program, which aims to maintain the capacity limits of the electricity grid and prevent grid congestion by financially incentivizing residential consumers to reduce their energy consumption. The proposed approach addresses the key challenge of coordinating heterogeneous preferences and requirements from multiple participants while preserving their privacy and minimizing financial costs for the aggregator. The participant agents use a novel Disjunctively Constrained Knapsack Problem optimization to curtail or shift the requested household appliances based on the selected demand reduction. Through case studies with electricity data from 25 households, the proposed approach effectively reduced energy consumption's Peak-to-Average ratio (PAR) by 14.48% compared to the original PAR while fully preserving participant privacy. This approach has the potential to significantly improve the efficiency and reliability of the electricity grid, making it an important contribution to the management of renewable energy resources and the growing electricity demand.