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 1–10 of 1718 papers

TitleStatusHype
Multi-Agent Reinforcement Learning for Autonomous Driving: A SurveyCode5
SigmaRL: A Sample-Efficient and Generalizable Multi-Agent Reinforcement Learning Framework for Motion PlanningCode4
Dispelling the Mirage of Progress in Offline MARL through Standardised Baselines and EvaluationCode3
Unreal-MAP: Unreal-Engine-Based General Platform for Multi-Agent Reinforcement LearningCode3
MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning LibraryCode3
On the Use and Misuse of Absorbing States in Multi-agent Reinforcement LearningCode3
AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-MakingCode2
DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement LearningCode2
Deep Reinforcement Learning for Multi-Agent InteractionCode2
Developing A Multi-Agent and Self-Adaptive Framework with Deep Reinforcement Learning for Dynamic Portfolio Risk ManagementCode2
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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