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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 501–510 of 1718 papers

TitleStatusHype
Fully Decentralized Cooperative Multi-Agent Reinforcement Learning: A Survey—0
A Tensor Network Implementation of Multi Agent Reinforcement Learning—0
ClusterComm: Discrete Communication in Decentralized MARL using Internal Representation Clustering—0
Exploiting hidden structures in non-convex games for convergence to Nash equilibrium—0
Adaptive trajectory-constrained exploration strategy for deep reinforcement learningCode0
Context-aware Communication for Multi-agent Reinforcement LearningCode1
CARSS: Cooperative Attention-guided Reinforcement Subpath Synthesis for Solving Traveling Salesman Problem—0
Dynamic Routing for Integrated Satellite-Terrestrial Networks: A Constrained Multi-Agent Reinforcement Learning Approach—0
Multi-Agent Reinforcement Learning for Assessing False-Data Injection Attacks on Transportation Networks—0
Sparse Mean Field Load Balancing in Large Localized Queueing 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