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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 351–375 of 1718 papers

TitleStatusHype
Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement LearningCode0
Coordination Failure in Cooperative Offline MARL—0
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
Tractable Equilibrium Computation in Markov Games through Risk Aversion—0
Soft-QMIX: Integrating Maximum Entropy For Monotonic Value Function FactorizationCode1
VELO: A Vector Database-Assisted Cloud-Edge Collaborative LLM QoS Optimization Framework—0
Balancing Performance and Cost for Two-Hop Cooperative Communications: Stackelberg Game and Distributed Multi-Agent Reinforcement LearningCode0
Communication-Efficient MARL for Platoon Stability and Energy-efficiency Co-optimization in Cooperative Adaptive Cruise Control of CAVs—0
Reconfigurable Intelligent Surface Assisted VEC Based on Multi-Agent Reinforcement LearningCode1
The Benefits of Power Regularization in Cooperative Reinforcement Learning—0
Dispelling the Mirage of Progress in Offline MARL through Standardised Baselines and EvaluationCode3
Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning—0
Carbon Market Simulation with Adaptive Mechanism DesignCode0
Multi-agent Reinforcement Learning with Deep Networks for Diverse Q-Vectors—0
Risk Sensitivity in Markov Games and Multi-Agent Reinforcement Learning: A Systematic Review—0
Adaptive Opponent Policy Detection in Multi-Agent MDPs: Real-Time Strategy Switch Identification Using Running Error Estimation—0
Mini Honor of Kings: A Lightweight Environment for Multi-Agent Reinforcement LearningCode2
Representation Learning For Efficient Deep Multi-Agent Reinforcement Learning—0
FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning—0
Multi-Agent Transfer Learning via Temporal Contrastive Learning—0
Multi-Agent Reinforcement Learning Meets Leaf Sequencing in Radiotherapy—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