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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 1091–1100 of 1718 papers

TitleStatusHype
Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning—0
CGIBNet: Bandwidth-constrained Communication with Graph Information Bottleneck in Multi-Agent Reinforcement Learning—0
Learning to Share in Multi-Agent Reinforcement LearningCode1
Finite-Sample Analysis of Decentralized Q-Learning for Stochastic Games—0
Multi-Agent Vulnerability Discovery for Autonomous Driving with Hazard Arbitration Reward—0
Cooperative Multi-Agent Reinforcement Learning with Hypergraph ConvolutionCode0
Self-Organized Polynomial-Time Coordination GraphsCode0
MDPGT: Momentum-based Decentralized Policy Gradient TrackingCode0
Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC TasksCode1
Reward-Free Attacks in 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