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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 231–240 of 1718 papers

TitleStatusHype
Mean-Field Sampling for Cooperative Multi-Agent Reinforcement Learning—0
Towards Fault Tolerance in Multi-Agent Reinforcement LearningCode0
A Local Information Aggregation based Multi-Agent Reinforcement Learning for Robot Swarm Dynamic Task Allocation—0
Integrating Transit Signal Priority into Multi-Agent Reinforcement Learning based Traffic Signal Control—0
Safe Multi-Agent Reinforcement Learning with Convergence to Generalized Nash Equilibrium—0
Multi-agent reinforcement learning strategy to maximize the lifetime of Wireless Rechargeable—0
Learning to Cooperate with Humans using Generative AgentsCode1
Adversarial Multi-Agent Reinforcement Learning for Proactive False Data Injection Detection—0
Efficient Training in Multi-Agent Reinforcement Learning: A Communication-Free Framework for the Box-Pushing Problem—0
Mitigating Relative Over-Generalization in 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