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 1451–1460 of 1718 papers

TitleStatusHype
Multi-Agent Reinforcement Learning in NOMA-aided UAV Networks for Cellular Offloading—0
Cooperative-Competitive Reinforcement Learning with History-Dependent Rewards—0
Multi-Agent Trust Region Policy OptimizationCode0
Leveraging the Capabilities of Connected and Autonomous Vehicles and Multi-Agent Reinforcement Learning to Mitigate Highway Bottleneck Congestion—0
Heterogeneous Multi-Agent Reinforcement Learning for Unknown Environment Mapping—0
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning—0
A Sharp Analysis of Model-based Reinforcement Learning with Self-Play—0
Correcting Experience Replay for Multi-Agent Communication—0
Emergent Social Learning via Multi-agent Reinforcement Learning—0
The Emergence of Individuality in Multi-Agent Reinforcement Learning—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