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 441–450 of 1718 papers

TitleStatusHype
Distributed Deep Reinforcement Learning for Functional Split Control in Energy Harvesting Virtualized Small Cells—0
Distributed Learning in Wireless Networks: Recent Progress and Future Challenges—0
Distributed Learning Meets 6G: A Communication and Computing Perspective—0
Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics—0
Distributed multi-agent target search and tracking with Gaussian process and reinforcement learning—0
Distributed Noncoherent Joint Transmission Based on Multi-Agent Reinforcement Learning for Dense Small Cell MISO Systems—0
Distributed off-Policy Actor-Critic Reinforcement Learning with Policy Consensus—0
Distributed Policy Gradient with Variance Reduction in Multi-Agent Reinforcement Learning—0
Distributed Policy Iteration for Scalable Approximation of Cooperative Multi-Agent Policies—0
Decentralized Multi-agent Reinforcement Learning based State-of-Charge Balancing Strategy for Distributed Energy Storage System—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