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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 131–140 of 1718 papers

TitleStatusHype
Revisiting the Gumbel-Softmax in MADDPGCode1
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority InfluenceCode1
Learning Zero-Shot Cooperation with Humans, Assuming Humans Are BiasedCode1
TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning ProblemsCode1
Learning to Control and Coordinate Mixed Traffic Through Robot Vehicles at Complex and Unsignalized IntersectionsCode1
Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative ExplorationCode1
Scalable Multi-Agent Reinforcement Learning for Warehouse Logistics with Robotic and Human Co-WorkersCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
Fleet Rebalancing for Expanding Shared e-Mobility Systems: A Multi-agent Deep Reinforcement Learning ApproachCode1
Scalable Multi-Agent Reinforcement Learning through Intelligent Information AggregationCode1
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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