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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 911–920 of 1718 papers

TitleStatusHype
Cooperative Actor-Critic via TD Error Aggregation—0
Cooperative and Competitive Biases for Multi-Agent Reinforcement Learning—0
Cooperative-Competitive Reinforcement Learning with History-Dependent Rewards—0
Scalable Multi-Agent Reinforcement Learning for Residential Load Scheduling under Data Governance—0
Cooperative Multi-Agent Assignment over Stochastic Graphs via Constrained Reinforcement Learning—0
Cooperative Multi-Agent Learning for Navigation via Structured State Abstraction—0
Cooperative Multi-Agent Planning with Adaptive Skill Synthesis—0
Cooperative Multi-Agent Reinforcement Learning for Low-Level Wireless Communication—0
Cooperative Multi-Agent Reinforcement Learning Framework for Scalping Trading—0
Cooperative Multi-Agent Reinforcement Learning with Partial Observations—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