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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 251–260 of 1718 papers

TitleStatusHype
Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement LearningCode1
AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-MakingCode2
Role Play: Learning Adaptive Role-Specific Strategies in Multi-Agent Interactions—0
Anytime-Constrained Equilibria in Polynomial Time—0
Demand-Aware Beam Hopping and Power Allocation for Load Balancing in Digital Twin empowered LEO Satellite Networks—0
Energy-Aware Multi-Agent Reinforcement Learning for Collaborative Execution in Mission-Oriented Drone Networks—0
A Multi-Agent Reinforcement Learning Testbed for Cognitive Radio Applications—0
Offline-to-Online Multi-Agent Reinforcement Learning with Offline Value Function Memory and Sequential Exploration—0
Multi-Agent Reinforcement Learning with Selective State-Space Models—0
Toward Finding Strong Pareto Optimal Policies in Multi-Agent Reinforcement LearningCode0
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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