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This paper presents a decentralized Multi-Agent Reinforcement Learning (MARL)approach to an incentive-based Demand Response (DR) program, which aims tomaintain the capacity limits of the electricity grid and prevent gridcongestion by financially incentivizing residential consumers to reduce theirenergy consumption. The proposed approach addresses the key challenge ofcoordinating heterogeneous preferences and requirements from multipleparticipants while preserving their privacy and minimizing financial costs forthe aggregator. The participant agents use a novel Disjunctively ConstrainedKnapsack Problem optimization to curtail or shift the requested householdappliances based on the selected demand reduction. Through case studies withelectricity data from $25$ households, the proposed approach effectivelyreduced energy consumption's Peak-to-Average ratio (PAR) by $14.48$% comparedto the original PAR while fully preserving participant privacy. This approachhas the potential to significantly improve the efficiency and reliability ofthe electricity grid, making it an important contribution to the management ofrenewable energy resources and the growing electricity demand.

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