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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 381–390 of 1718 papers

TitleStatusHype
Individual Contributions as Intrinsic Exploration Scaffolds for Multi-agent Reinforcement LearningCode1
PyTAG: Tabletop Games for Multi-Agent Reinforcement LearningCode1
Variational Offline Multi-agent Skill Discovery—0
M-RAG: Reinforcing Large Language Model Performance through Retrieval-Augmented Generation with Multiple Partitions—0
eQMARL: Entangled Quantum Multi-Agent Reinforcement Learning for Distributed Cooperation over Quantum ChannelsCode0
Controlling Behavioral Diversity in Multi-Agent Reinforcement LearningCode1
A finite time analysis of distributed Q-learning—0
Multi-Agent Reinforcement Learning with Hierarchical Coordination for Emergency Responder Stationing—0
Efficient Multi-agent Reinforcement Learning by PlanningCode1
LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions—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