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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 521–530 of 1718 papers

TitleStatusHype
Episodic Future Thinking Mechanism for Multi-agent Reinforcement Learning—0
Hierarchical Multi-agent Reinforcement Learning for Cyber Network Defense—0
Convex Markov Games: A New Frontier for Multi-Agent Reinforcement Learning—0
Evolution of Societies via Reinforcement LearningCode0
FlickerFusion: Intra-trajectory Domain Generalizing Multi-Agent RL—0
A Distributed Primal-Dual Method for Constrained Multi-agent Reinforcement Learning with General Parameterization—0
Large Legislative Models: Towards Efficient AI Policymaking in Economic SimulationsCode0
Learning to Balance Altruism and Self-interest Based on Empathy in Mixed-Motive Games—0
Variational Inequality Methods for Multi-Agent Reinforcement Learning: Performance and Stability Gains—0
CAFEEN: A Cooperative Approach for Energy Efficient NoCs with Multi-Agent Reinforcement Learning—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