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 1501–1510 of 1718 papers

TitleStatusHype
DeepSafeMPC: Deep Learning-Based Model Predictive Control for Safe Multi-Agent Reinforcement Learning—0
Demand-Aware Beam Hopping and Power Allocation for Load Balancing in Digital Twin empowered LEO Satellite Networks—0
Density-Aware Reinforcement Learning to Optimise Energy Efficiency in UAV-Assisted Networks—0
Dependent Multi-Task Learning with Causal Intervention for Image Captioning—0
Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity—0
Dialogue Management based on Multi-domain Corpus—0
DIAMOND: Taming Sample and Communication Complexities in Decentralized Bilevel Optimization—0
Difference Rewards Policy Gradients—0
Differentiable Arbitrating in Zero-sum Markov Games—0
Differentially Private Reinforcement Learning with Self-Play—0
Show:102550
← PrevPage 151 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