Nash-MADDPG combines Nash bargaining with MADDPG to coordinate V2V energy trades, yielding 61.6% higher social welfare and 40.1% better Jain fairness than double auctions in 30-day simulations with 6-100 agents.
A quantitative measure of fairness and discrimination
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
method 1
citation-polarity summary
years
2026 4verdicts
UNVERDICTED 4roles
method 1polarities
use method 1representative citing papers
A unified alpha-fairness scheme for clustered cell-free networking is achieved via closed-form deterministic equivalents and tight continuous relaxations that are exactly equivalent to the integer program in four cases of alpha.
A convex framework for partially coordinated dynamic operating envelopes increases aggregate active-power injection range by ~25% when 30% of customers coordinate, while respecting network limits, fairness, and forecast uncertainty.
An algorithm computes optimal action recommendations via backward induction over a family of linear programs so that obedient play is sequentially rational for both agents.
citing papers explorer
-
Incentive-Aligned Vehicle-to-Vehicle Energy Trading via Nash-Integrated Multi-Agent Reinforcement Learning
Nash-MADDPG combines Nash bargaining with MADDPG to coordinate V2V energy trades, yielding 61.6% higher social welfare and 40.1% better Jain fairness than double auctions in 30-day simulations with 6-100 agents.
-
Achieving $\alpha$-Fairness in Clustered Cell-Free Networking: A Tight Relaxation Approach
A unified alpha-fairness scheme for clustered cell-free networking is achieved via closed-form deterministic equivalents and tight continuous relaxations that are exactly equivalent to the integer program in four cases of alpha.
-
Coordinated Dynamic Operating Envelopes for Unlocking Additional Flexibility at Grid Edge
A convex framework for partially coordinated dynamic operating envelopes increases aggregate active-power injection range by ~25% when 30% of customers coordinate, while respecting network limits, fairness, and forecast uncertainty.
-
Action Recommendations for Sequentially Rational Strategic Agents
An algorithm computes optimal action recommendations via backward induction over a family of linear programs so that obedient play is sequentially rational for both agents.