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 1501–1510 of 1718 papers

TitleStatusHype
Multi-Agent Informational Learning Processes—0
Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward—0
Multi-Agent Reinforcement Learning in Stochastic Networked SystemsCode0
Multi-Agent Reinforcement Learning in a Realistic Limit Order Book Market Simulation—0
Skill Discovery of Coordination in Multi-agent Reinforcement Learning—0
Incorporating Pragmatic Reasoning Communication into Emergent Language—0
Towards Understanding Cooperative Multi-Agent Q-Learning with Value Factorization—0
Revisiting Parameter Sharing in Multi-Agent Deep Reinforcement LearningCode0
Batch-Augmented Multi-Agent Reinforcement Learning for Efficient Traffic Signal Optimization—0
Experience Augmentation: Boosting and Accelerating Off-Policy Multi-Agent Reinforcement Learning—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