SOTAVerified

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 121–130 of 1718 papers

TitleStatusHype
Pretrained LLMs as Real-Time Controllers for Robot Operated Serial Production Line—0
Human Implicit Preference-Based Policy Fine-tuning for Multi-Agent Reinforcement Learning in USV Swarm—0
Decentralized Reinforcement Learning for Multi-Agent Multi-Resource Allocation via Dynamic Cluster Agreements—0
Towards Robust Multi-UAV Collaboration: MARL with Noise-Resilient Communication and Attention MechanismsCode0
Trajectory-Class-Aware Multi-Agent Reinforcement LearningCode1
M3HF: Multi-agent Reinforcement Learning from Multi-phase Human Feedback of Mixed Quality—0
SrSv: Integrating Sequential Rollouts with Sequential Value Estimation for Multi-agent Reinforcement Learning—0
Multi-Agent Reinforcement Learning with Long-Term Performance Objectives for Service Workforce Optimization—0
Real-World Deployment and Assessment of a Multi-Agent Reinforcement Learning-Based Variable Speed Limit Control SystemCode0
Factorized Deep Q-Network for Cooperative Multi-Agent Reinforcement Learning in Victim Tagging—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