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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1311–1320 of 1718 papers

TitleStatusHype
Multi-Agent Reinforcement Learning with Selective State-Space Models—0
Multi-Agent Reinforcement Learning with a Hierarchy of Reward Machines—0
Multi-agent Reinforcement Learning with Deep Networks for Diverse Q-Vectors—0
BGC: Multi-Agent Group Belief with Graph Clustering—0
Multi-Agent Reinforcement Learning with Graph Convolutional Neural Networks for optimal Bidding Strategies of Generation Units in Electricity Markets—0
Multi-agent Reinforcement Learning with Graph Q-Networks for Antenna Tuning—0
Multi-Agent Reinforcement Learning with Multi-Step Generative Models—0
Multi-Agent RL-Based Industrial AIGC Service Offloading over Wireless Edge Networks—0
Emergent Social Learning via Multi-agent Reinforcement Learning—0
Multi-Agent Transfer Learning via Temporal Contrastive Learning—0
Show:102550
← PrevPage 132 of 172Next →
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