SOTAVerified

Multi-agent Reinforcement Learning

The target of Multi-agent Reinforcement Learning is to solve complex problems by integrating multiple agents that focus on different sub-tasks. In general, there are two types of multi-agent systems: independent and cooperative systems.

Source: Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports

Papers

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Showing 1511–1520 of 1718 papers

TitleStatusHype
Learning to Resolve Alliance Dilemmas in Many-Player Zero-Sum Games—0
Learning Scalable Multi-Agent Coordination by Spatial Differentiation for Traffic Signal ControlCode1
Multi-Agent Reinforcement Learning as a Computational Tool for Language Evolution Research: Historical Context and Future Challenges—0
Reward Design for Driver Repositioning Using Multi-Agent Reinforcement Learning—0
Extended Markov Games to Learn Multiple Tasks in Multi-Agent Reinforcement LearningCode0
Multi-Vehicle Routing Problems with Soft Time Windows: A Multi-Agent Reinforcement Learning Approach—0
Learning Multi-Agent Coordination through Connectivity-driven Communication—0
Learning Structured Communication for Multi-agent Reinforcement Learning—0
Mean-Field Controls with Q-learning for Cooperative MARL: Convergence and Complexity Analysis—0
Proficiency Constrained Multi-Agent Reinforcement Learning for Environment-Adaptive Multi UAV-UGV Teaming—0
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All datasetsParticleEnvs Cooperative CommunicationSMAC-ExpUAV Logistics

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified