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 1141–1150 of 1718 papers

TitleStatusHype
Learning to Communicate with Reinforcement Learning for an Adaptive Traffic Control System—0
Mixed Cooperative-Competitive Communication Using Multi-Agent Reinforcement Learning—0
Crowd-sensing Enhanced Parking Patrol using Trajectories of Sharing Bikes—0
A Law of Iterated Logarithm for Multi-Agent Reinforcement Learning—0
Model based Multi-agent Reinforcement Learning with Tensor Decompositions—0
Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution NetworksCode1
Reinforcement Learning in Factored Action Spaces using Tensor Decompositions—0
Multi-Agent Advisor Q-LearningCode0
Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey—0
Learning to Simulate Self-Driven Particles System with Coordinated Policy OptimizationCode1
Show:102550
← PrevPage 115 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