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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 921–930 of 1718 papers

TitleStatusHype
Privacy-Preserving Joint Edge Association and Power Optimization for the Internet of Vehicles via Federated Multi-Agent Reinforcement Learning—0
Privacy Preserving Multi-Agent Reinforcement Learning in Supply Chains—0
Proactive Multi-Camera Collaboration For 3D Human Pose Estimation—0
Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning—0
Probabilistic View of Multi-agent Reinforcement Learning: A Unified Approach—0
Probe-Based Interventions for Modifying Agent Behavior—0
Proficiency Constrained Multi-Agent Reinforcement Learning for Environment-Adaptive Multi UAV-UGV Teaming—0
Promoting Cooperation in Multi-Agent Reinforcement Learning via Mutual Help—0
Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning—0
Collaboration Promotes Group Resilience in Multi-Agent AI—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