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 1491–1500 of 1718 papers

TitleStatusHype
QTRAN++: Improved Value Transformation for Cooperative Multi-Agent Reinforcement Learning—0
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning—0
Efficient Ridesharing Dispatch Using Multi-Agent Reinforcement LearningCode0
Distributed Value Function Approximation for Collaborative Multi-Agent Reinforcement Learning—0
Cooperative Multi-Agent Reinforcement Learning with Partial Observations—0
Policy Evaluation and Seeking for Multi-Agent Reinforcement Learning via Best Response—0
Eco-Vehicular Edge Networks for Connected Transportation: A Distributed Multi-Agent Reinforcement Learning Approach—0
Multi-Agent Reinforcement Learning for Adaptive User Association in Dynamic mmWave Networks—0
Human and Multi-Agent collaboration in a human-MARL teaming framework—0
Learning to Communicate Using Counterfactual Reasoning—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