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 531–540 of 1718 papers

TitleStatusHype
Cluster-Based Multi-Agent Task Scheduling for Space-Air-Ground Integrated Networks—0
Anytime PSRO for Two-Player Zero-Sum Games—0
Age Minimization in Massive IoT via UAV Swarm: A Multi-agent Reinforcement Learning Approach—0
Efficient Adversarial Attacks on Online Multi-agent Reinforcement Learning—0
Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning—0
Closure Discovery for Coarse-Grained Partial Differential Equations Using Grid-based Reinforcement Learning—0
EdgeML: Towards Network-Accelerated Federated Learning over Wireless Edge—0
CH-MARL: A Multimodal Benchmark for Cooperative, Heterogeneous Multi-Agent Reinforcement Learning—0
Anytime-Constrained Equilibria in Polynomial Time—0
EdgeAgentX: A Novel Framework for Agentic AI at the Edge in Military Communication Networks—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