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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 1301–1310 of 1718 papers

TitleStatusHype
Architectural Influence on Variational Quantum Circuits in Multi-Agent Reinforcement Learning: Evolutionary Strategies for Optimization—0
Dynamic Co-Optimization Compiler: Leveraging Multi-Agent Reinforcement Learning for Enhanced DNN Accelerator Performance—0
A Regulation Enforcement Solution for Multi-agent Reinforcement Learning—0
A Reinforcement Learning Approach to Quiet and Safe UAM Traffic Management—0
A Review of Cooperative Multi-Agent Deep Reinforcement Learning—0
A Review of Deep Reinforcement Learning in Serverless Computing: Function Scheduling and Resource Auto-Scaling—0
A Review of the Applications of Deep Learning-Based Emergent Communication—0
Argus: Smartphone-enabled Human Cooperation via Multi-Agent Reinforcement Learning for Disaster Situational Awareness—0
A Roadmap Towards Improving Multi-Agent Reinforcement Learning With Causal Discovery And Inference—0
A Robust and Constrained Multi-Agent Reinforcement Learning Electric Vehicle Rebalancing Method in AMoD Systems—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