Incentive-Aligned Vehicle-to-Vehicle Energy Trading via Nash-Integrated Multi-Agent Reinforcement Learning
Vehicle-to-vehicle (V2V) energy trading enables decentralized peer-to-peer energy exchange among electric vehicles (EVs), reducing grid dependency while monetizing surplus capacity. However, coordinating self-interested EV agents with diverse charging needs and uncertain arrival-departure schedules remains challenging. Existing approaches either require centralized optimization with computational limitations or lack fairness guarantees. This paper integrates Nash Bargaining Solution into Multi-A
Record details
Published: 21 May 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Agents & autonomy · Environment
Retrieved: 14 July 2026
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How to cite this record
ethics.ai (21 May 2026), “Incentive-Aligned Vehicle-to-Vehicle Energy Trading via Nash-Integrated Multi-Agent Reinforcement Learning,” evidence record 3926, https://ethics.ai/record/3926 (originally published by arXiv).
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