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.
Multi-agent actor-critic for mixed cooperative-competitive environ- ments
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
ARMATA is a new end-to-end autoregressive model with multi-stage decoding that unifies allocation and routing for multi-agent systems and reports up to 20% better solutions than OR-Tools, CPLEX, and LKH-3 in seconds instead of hours.
citing papers explorer
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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.
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ARMATA: Auto-Regressive Multi-Agent Task Assignment
ARMATA is a new end-to-end autoregressive model with multi-stage decoding that unifies allocation and routing for multi-agent systems and reports up to 20% better solutions than OR-Tools, CPLEX, and LKH-3 in seconds instead of hours.