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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 221–230 of 1718 papers

TitleStatusHype
Steganography in Game Actions—0
Learn How to Query from Unlabeled Data Streams in Federated LearningCode0
Offline Multi-Agent Reinforcement Learning via In-Sample Sequential Policy Optimization—0
Augmenting the action space with conventions to improve multi-agent cooperation in HanabiCode0
HyperMARL: Adaptive Hypernetworks for Multi-Agent RLCode1
Intersection-Aware Assessment of EMS Accessibility in NYC: A Data-Driven Approach—0
Traffic Co-Simulation Framework Empowered by Infrastructure Camera Sensing and Reinforcement Learning—0
Reinforcement Learning for Freeway Lane-Change Regulation via Connected Vehicles—0
Comparative Analysis of Multi-Agent Reinforcement Learning Policies for Crop Planning Decision Support—0
The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis—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