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 351–360 of 1718 papers

TitleStatusHype
Coordination Failure in Cooperative Offline MARL—0
Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement LearningCode0
Diffusion Models for Offline Multi-agent Reinforcement Learning with Safety Constraints—0
Temporal Prototype-Aware Learning for Active Voltage Control on Power Distribution NetworksCode1
CuDA2: An approach for Incorporating Traitor Agents into Cooperative Multi-Agent Systems—0
Quantum Multi-Agent Reinforcement Learning for Cooperative Mobile Access in Space-Air-Ground Integrated Networks—0
Adaptive Digital Twin and Communication-Efficient Federated Learning Network Slicing for 5G-enabled Internet of Things—0
Soft-QMIX: Integrating Maximum Entropy For Monotonic Value Function FactorizationCode1
Tractable Equilibrium Computation in Markov Games through Risk Aversion—0
VELO: A Vector Database-Assisted Cloud-Edge Collaborative LLM QoS Optimization Framework—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