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 401–410 of 1718 papers

TitleStatusHype
Enhancing Cooperation through Selective Interaction and Long-term Experiences in Multi-Agent Reinforcement LearningCode1
Linear Convergence of Independent Natural Policy Gradient in Games with Entropy Regularization—0
Taming Equilibrium Bias in Risk-Sensitive Multi-Agent Reinforcement Learning—0
SocialGFs: Learning Social Gradient Fields for Multi-Agent Reinforcement Learning—0
Simulating the Economic Impact of Rationality through Reinforcement Learning and Agent-Based ModellingCode1
MESA: Cooperative Meta-Exploration in Multi-Agent Learning through Exploiting State-Action Space Structure—0
MF-OML: Online Mean-Field Reinforcement Learning with Occupation Measures for Large Population Games—0
Provably Efficient Information-Directed Sampling Algorithms for Multi-Agent Reinforcement Learning—0
Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty—0
Verco: Learning Coordinated Verbal Communication for Multi-agent Reinforcement Learning—0
Show:102550
← PrevPage 41 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