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 1191–1200 of 1718 papers

TitleStatusHype
Verco: Learning Coordinated Verbal Communication for Multi-agent Reinforcement Learning—0
Vision-Based Generic Potential Function for Policy Alignment in Multi-Agent Reinforcement Learning—0
Software Simulation and Visualization of Quantum Multi-Drone Reinforcement Learning—0
Visual Theory of Mind Enables the Invention of Proto-Writing—0
VolleyBots: A Testbed for Multi-Drone Volleyball Game Combining Motion Control and Strategic Play—0
Voting-Based Multi-Agent Reinforcement Learning for Intelligent IoT—0
Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning—0
When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently?—0
When Is Diversity Rewarded in Cooperative Multi-Agent Learning?—0
When is Offline Two-Player Zero-Sum Markov Game Solvable?—0
Show:102550
← PrevPage 120 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