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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 1221–1230 of 1718 papers

TitleStatusHype
Accelerate Multi-Agent Reinforcement Learning in Zero-Sum Games with Subgame Curriculum Learning—0
ACCNet: Actor-Coordinator-Critic Net for "Learning-to-Communicate" with Deep Multi-agent Reinforcement Learning—0
Achieving Collective Welfare in Multi-Agent Reinforcement Learning via Suggestion Sharing—0
Achieving Optimal Tissue Repair Through MARL with Reward Shaping and Curriculum Learning—0
A Collaborative Multi-agent Reinforcement Learning Framework for Dialog Action Decomposition—0
A Comprehensive Survey on Multi-Agent Cooperative Decision-Making: Scenarios, Approaches, Challenges and Perspectives—0
Action Dependency Graphs for Globally Optimal Coordinated Reinforcement Learning—0
Active flow control for three-dimensional cylinders through deep reinforcement learning—0
ADAGE: A generic two-layer framework for adaptive agent based modelling—0
Adaptive AI-based Decentralized Resource Management in the Cloud-Edge Continuum—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