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 771–780 of 1718 papers

TitleStatusHype
DePAint: A Decentralized Safe Multi-Agent Reinforcement Learning Algorithm considering Peak and Average ConstraintsCode0
Dynamic Resource Management in Integrated NOMA Terrestrial-Satellite Networks using Multi-Agent Reinforcement Learning—0
MARVEL: Multi-Agent Reinforcement-Learning for Large-Scale Variable Speed Limits—0
Fact-based Agent modeling for Multi-Agent Reinforcement Learning—0
Combat Urban Congestion via Collaboration: Heterogeneous GNN-based MARL for Coordinated Platooning and Traffic Signal Control—0
Robust Multi-Agent Reinforcement Learning by Mutual Information Regularization—0
Robustness to Multi-Modal Environment Uncertainty in MARL using Curriculum Learning—0
Quantifying Agent Interaction in Multi-agent Reinforcement Learning for Cost-efficient Generalization—0
Sample-Efficient Multi-Agent RL: An Optimization Perspective—0
Replication of Multi-agent Reinforcement Learning for the "Hide and Seek" Problem—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