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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 431–440 of 1718 papers

TitleStatusHype
Safety-Aware Multi-Agent Learning for Dynamic Network Bridging—0
Emergence of Chemotactic Strategies with Multi-Agent Reinforcement Learning—0
GOV-REK: Governed Reward Engineering Kernels for Designing Robust Multi-Agent Reinforcement Learning SystemsCode0
Inferring Latent Temporal Sparse Coordination Graph for Multi-Agent Reinforcement LearningCode0
Self-Clustering Hierarchical Multi-Agent Reinforcement Learning with Extensible Cooperation Graph—0
Paths to Equilibrium in Games—0
MA4DIV: Multi-Agent Reinforcement Learning for Search Result Diversification—0
Sample and Communication Efficient Fully Decentralized MARL Policy Evaluation via a New Approach: Local TD update—0
Multi-agent transformer-accelerated RL for satisfaction of STL specifications—0
Carbon Footprint Reduction for Sustainable Data Centers in Real-Time—0
Show:102550
← PrevPage 44 of 172Next →
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