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 721–730 of 1718 papers

TitleStatusHype
Cooperation and Competition: Flocking with Evolutionary Multi-Agent Reinforcement Learning—0
Impression Allocation and Policy Search in Display Advertising—0
Few is More: Task-Efficient Skill-Discovery for Multi-Task Offline Multi-Agent Reinforcement Learning—0
Improving Global Parameter-sharing in Physically Heterogeneous Multi-agent Reinforcement Learning with Unified Action Space—0
Improving International Climate Policy via Mutually Conditional Binding Commitments—0
Fever Basketball: A Complex, Flexible, and Asynchronized Sports Game Environment for Multi-agent Reinforcement Learning—0
Improving the generalizability and robustness of large-scale traffic signal control—0
Incentivize without Bonus: Provably Efficient Model-based Online Multi-agent RL for Markov Games—0
Incorporating Pragmatic Reasoning Communication into Emergent Language—0
Convex Markov Games: A New Frontier for Multi-Agent Reinforcement Learning—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