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 1371–1380 of 1718 papers

TitleStatusHype
Low-Bandwidth Communication Emerges Naturally in Multi-Agent Learning Systems—0
Minimax Sample Complexity for Turn-based Stochastic Game—0
TLeague: A Framework for Competitive Self-Play based Distributed Multi-Agent Reinforcement LearningCode1
PowerNet: Multi-agent Deep Reinforcement Learning for Scalable Powergrid Control—0
Multi-Agent Reinforcement Learning for Markov Routing Games: A New Modeling Paradigm For Dynamic Traffic Assignment—0
Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?Code1
Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networks—0
Multi-agent Reinforcement Learning Accelerated MCMC on Multiscale Inversion Problem—0
Scalable Reinforcement Learning Policies for Multi-Agent ControlCode1
Optimizing Large-Scale Fleet Management on a Road Network using Multi-Agent Deep Reinforcement Learning with Graph Neural NetworkCode1
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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