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 1241–1250 of 1718 papers

TitleStatusHype
Multi-Agent Hybrid SAC for Joint SS-DSA in CRNs—0
Multi-Agent Informational Learning Processes—0
Multi-Agent Language Models: Advancing Cooperation, Coordination, and Adaptation—0
Multi-agent Natural Actor-critic Reinforcement Learning Algorithms—0
Multi-Agent Neural Rewriter for Vehicle Routing with Limited Disclosure of Costs—0
Multi-agent Off-policy Actor-Critic Reinforcement Learning for Partially Observable Environments—0
An Energy-aware and Fault-tolerant Deep Reinforcement Learning based approach for Multi-agent Patrolling Problems—0
Multi-agent Policy Optimization with Approximatively Synchronous Advantage Estimation—0
Multi-agent Policy Reciprocity with Theoretical Guarantee—0
Multi-agent Reinforcement Learning Accelerated MCMC on Multiscale Inversion Problem—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