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 991–1000 of 1718 papers

TitleStatusHype
Multi-agent Databases via Independent Learning—0
ALMA: Hierarchical Learning for Composite Multi-Agent TasksCode1
Off-Beat Multi-Agent Reinforcement Learning—0
Feudal Multi-Agent Reinforcement Learning with Adaptive Network Partition for Traffic Signal Control—0
Scalable Multi-Agent Model-Based Reinforcement LearningCode1
Trust-based Consensus in Multi-Agent Reinforcement Learning Systems—0
QGNN: Value Function Factorisation with Graph Neural NetworksCode1
MAVIPER: Learning Decision Tree Policies for Interpretable Multi-Agent Reinforcement Learning—0
Graph Convolutional Reinforcement Learning for Collaborative Queuing Agents—0
Learning to Advise and Learning from Advice in Cooperative Multi-Agent Reinforcement Learning—0
Show:102550
← PrevPage 100 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