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 571–580 of 1718 papers

TitleStatusHype
Accelerate Multi-Agent Reinforcement Learning in Zero-Sum Games with Subgame Curriculum Learning—0
Self-Confirming Transformer for Belief-Conditioned Adaptation in Offline Multi-Agent Reinforcement Learning—0
Deconstructing Cooperation and Ostracism via Multi-Agent Reinforcement Learning—0
Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement LearningCode1
A Review of Deep Reinforcement Learning in Serverless Computing: Function Scheduling and Resource Auto-Scaling—0
Fictitious Cross-Play: Learning Global Nash Equilibrium in Mixed Cooperative-Competitive Games—0
Multi-Agent Reinforcement Learning for Power Grid Topology OptimizationCode0
Multi-Agent Reinforcement Learning Based on Representational Communication for Large-Scale Traffic Signal Control—0
COMPOSER: Scalable and Robust Modular Policies for Snake Robots—0
Cooperation Dynamics in Multi-Agent Systems: Exploring Game-Theoretic Scenarios with Mean-Field EquilibriaCode0
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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