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

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Showing 711–720 of 1718 papers

TitleStatusHype
Information Structure in Mappings: An Approach to Learning, Representation, and Generalisation—0
Metric Policy Representations for Opponent Modeling—0
Integrating Transit Signal Priority into Multi-Agent Reinforcement Learning based Traffic Signal Control—0
IA-MARL: Imputation Assisted Multi-Agent Reinforcement Learning for Missing Training Data—0
IntelligentCrowd: Mobile Crowdsensing via Multi-Agent Reinforcement Learning—0
ICCO: Learning an Instruction-conditioned Coordinator for Language-guided Task-aligned Multi-robot Control—0
Fictitious Cross-Play: Learning Global Nash Equilibrium in Mixed Cooperative-Competitive Games—0
Imagine, Initialize, and Explore: An Effective Exploration Method in Multi-Agent Reinforcement Learning—0
Few-Shot Teamwork—0
Cooperation and Competition: Flocking with Evolutionary Multi-Agent Reinforcement Learning—0
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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