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 1171–1180 of 1718 papers

TitleStatusHype
Ultra-dense Low Data Rate (UDLD) Communication in the THz—0
Truthful Self-Play—0
Partner Selection for the Emergence of Cooperation in Multi-Agent Systems Using Reinforcement Learning—0
Understanding the World to Solve Social Dilemmas Using Multi-Agent Reinforcement Learning—0
Understanding Value Decomposition Algorithms in Deep Cooperative Multi-Agent Reinforcement Learning—0
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning—0
UNEX-RL: Reinforcing Long-Term Rewards in Multi-Stage Recommender Systems with UNidirectional EXecution—0
Unicorn: A Universal and Collaborative Reinforcement Learning Approach Towards Generalizable Network-Wide Traffic Signal Control—0
Universal Agent Mixtures and the Geometry of Intelligence—0
Multi-agent Attacks for Black-box Social Recommendations—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