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 1131–1140 of 1718 papers

TitleStatusHype
The challenge of redundancy on multi-agent value factorisation—0
The Complexity of Markov Equilibrium in Stochastic Games—0
The Emergence of Individuality in Multi-Agent Reinforcement Learning—0
The Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learning—0
Interpretable Learned Emergent Communication for Human-Agent Teams—0
The Gradient Convergence Bound of Federated Multi-Agent Reinforcement Learning with Efficient Communication—0
The Multi-Agent Pickup and Delivery Problem: MAPF, MARL and Its Warehouse Applications—0
Theory of Mind as Intrinsic Motivation for Multi-Agent Reinforcement Learning—0
Theory of Minds: Understanding Behavior in Groups Through Inverse Planning—0
The Power of Communication in a Distributed Multi-Agent System—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