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 901–910 of 1718 papers

TitleStatusHype
SocialLight: Distributed Cooperation Learning towards Network-Wide Traffic Signal Control—0
Mastering Asymmetrical Multiplayer Game with Multi-Agent Asymmetric-Evolution Reinforcement Learning—0
Interpretability for Conditional Coordinated Behavior in Multi-Agent Reinforcement Learning—0
Graph Exploration for Effective Multi-agent Q-Learning—0
Cooperative Multi-Agent Reinforcement Learning for Inventory Management—0
Sustainable AIGC Workload Scheduling of Geo-Distributed Data Centers: A Multi-Agent Reinforcement Learning Approach—0
Model-based Dynamic Shielding for Safe and Efficient Multi-Agent Reinforcement Learning—0
Learning to Communicate and Collaborate in a Competitive Multi-Agent Setup to Clean the Ocean from Macroplastics—0
Multi-agent Policy Reciprocity with Theoretical Guarantee—0
MABL: Bi-Level Latent-Variable World Model for Sample-Efficient Multi-Agent Reinforcement Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified