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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 611–620 of 1718 papers

TitleStatusHype
Multi-Agent Transfer Learning via Temporal Contrastive Learning—0
Fusion-PSRO: Nash Policy Fusion for Policy Space Response Oracles—0
Safe Multi-agent Reinforcement Learning with Natural Language Constraints—0
Efficient Learning in Chinese Checkers: Comparing Parameter Sharing in Multi-Agent Reinforcement LearningCode0
Mutation-Bias Learning in Games—0
M-RAG: Reinforcing Large Language Model Performance through Retrieval-Augmented Generation with Multiple Partitions—0
Variational Offline Multi-agent Skill Discovery—0
eQMARL: Entangled Quantum Multi-Agent Reinforcement Learning for Distributed Cooperation over Quantum ChannelsCode0
A finite time analysis of distributed Q-learning—0
Multi-Agent Reinforcement Learning with Hierarchical Coordination for Emergency Responder Stationing—0
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Benchmark Results

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