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 1671–1680 of 1718 papers

TitleStatusHype
Functional Optimization Reinforcement Learning for Real-Time Bidding—0
Function Approximation for Reinforcement Learning Controller for Energy from Spread Waves—0
Fusion-PSRO: Nash Policy Fusion for Policy Space Response Oracles—0
Game Theory and Multi-Agent Reinforcement Learning : From Nash Equilibria to Evolutionary Dynamics—0
Generalisable Agents for Neural Network Optimisation—0
Generalization in Cooperative Multi-Agent Systems—0
General sum stochastic games with networked information flows—0
Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles—0
Generative Emergent Communication: Large Language Model is a Collective World Model—0
Global Convergence of Localized Policy Iteration in Networked 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