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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 1601–1610 of 1718 papers

TitleStatusHype
Environmental-Impact Based Multi-Agent Reinforcement Learning—0
Episodic Future Thinking Mechanism for Multi-agent Reinforcement Learning—0
Escaping the State of Nature: A Hobbesian Approach to Cooperation in Multi-agent Reinforcement Learning—0
ESP: Exploiting Symmetry Prior for Multi-Agent Reinforcement Learning—0
Evaluating Generalization and Transfer Capacity of Multi-Agent Reinforcement Learning Across Variable Number of Agents—0
Evaluating Robustness of Cooperative MARL—0
Attacking c-MARL More Effectively: A Data Driven Approach—0
Evaluating Uncertainties in Electricity Markets via Machine Learning and Quantum Computing—0
Evolutionary Dispersal of Ecological Species via Multi-Agent Deep Reinforcement Learning—0
Evolutionary Reinforcement Learning: A Survey—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