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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 1321–1330 of 1718 papers

TitleStatusHype
Influence-Based Reinforcement Learning for Intrinsically-Motivated Agents—0
The Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learning—0
Mis-spoke or mis-lead: Achieving Robustness in Multi-Agent Communicative Reinforcement Learning—0
Semantic Tracklets: An Object-Centric Representation for Visual Multi-Agent Reinforcement Learning—0
Mean-Field Multi-Agent Reinforcement Learning: A Decentralized Network Approach—0
Offline Decentralized Multi-Agent Reinforcement Learning—0
Flip Learning: Erase to Segment—0
Survey of Recent Multi-Agent Reinforcement Learning Algorithms Utilizing Centralized Training—0
Packet Routing with Graph Attention Multi-agent Reinforcement Learning—0
Improved Reinforcement Learning in Cooperative Multi-agent Environments Using Knowledge Transfer—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