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 1301–1310 of 1718 papers

TitleStatusHype
Local Patch AutoAugment with Multi-Agent CollaborationCode1
Adversarial attacks in consensus-based multi-agent reinforcement learning—0
The AI Arena: A Framework for Distributed Multi-Agent Reinforcement LearningCode1
A multi-agent reinforcement learning model of reputation and cooperation in human groups—0
Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximation—0
DeepFreight: Integrating Deep Reinforcement Learning and Mixed Integer Programming for Multi-transfer Truck Freight DeliveryCode1
Efficient UAV Trajectory-Planning using Economic Reinforcement Learning—0
Learning to Fly -- a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter ControlCode2
The Surprising Effectiveness of PPO in Cooperative, Multi-Agent GamesCode1
Multi-agent Reinforcement Learning in OpenSpiel: A Reproduction ReportCode1
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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