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 361–370 of 1718 papers

TitleStatusHype
A multi-agent reinforcement learning model of reputation and cooperation in human groups—0
Boosting Sample Efficiency and Generalization in Multi-agent Reinforcement Learning via Equivariance—0
BMG-Q: Localized Bipartite Match Graph Attention Q-Learning for Ride-Pooling Order Dispatch—0
Analysing Congestion Problems in Multi-agent Reinforcement Learning—0
Birds of a Feather Flock Together: A Close Look at Cooperation Emergence via Multi-Agent RL—0
An Algorithm For Adversary Aware Decentralized Networked MARL—0
Achieving Optimal Tissue Repair Through MARL with Reward Shaping and Curriculum Learning—0
Achieving Collective Welfare in Multi-Agent Reinforcement Learning via Suggestion Sharing—0
Bi-level Mean Field: Dynamic Grouping for Large-Scale MARL—0
MABL: Bi-Level Latent-Variable World Model for Sample-Efficient 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