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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 1001–1010 of 1718 papers

TitleStatusHype
Coordinating Policies Among Multiple Agents via an Intelligent Communication Channel—0
Learning Progress Driven Multi-Agent CurriculumCode0
Sparse Adversarial Attack in Multi-agent Reinforcement Learning—0
Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam SearchCode1
Distributed Transmission Control for Wireless Networks using Multi-Agent Reinforcement LearningCode0
Emergent Bartering Behaviour in Multi-Agent Reinforcement Learning—0
Efficient Distributed Framework for Collaborative Multi-Agent Reinforcement Learning—0
Multi-Target Active Object Tracking with Monte Carlo Tree Search and Target Motion Modeling—0
LDSA: Learning Dynamic Subtask Assignment in Cooperative Multi-Agent Reinforcement Learning—0
General sum stochastic games with networked information flows—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