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 1141–1150 of 1718 papers

TitleStatusHype
Toward Multi-Agent Reinforcement Learning for Distributed Event-Triggered Control—0
Toward Real-World Cooperative and Competitive Soccer with Quadrupedal Robot Teams—0
Toward Risk-based Optimistic Exploration for Cooperative Multi-Agent Reinforcement Learning—0
Towards Adaptive IMFs -- Generalization of utility functions in Multi-Agent Frameworks—0
Towards Better Sample Efficiency in Multi-Agent Reinforcement Learning via Exploration—0
Towards Collaborative Intelligence: Propagating Intentions and Reasoning for Multi-Agent Coordination with Large Language Models—0
Towards Comprehensive Testing on the Robustness of Cooperative Multi-agent Reinforcement Learning—0
Towards Global Optimality in Cooperative MARL with the Transformation And Distillation Framework—0
Towards Heterogeneous Multi-Agent Reinforcement Learning with Graph Neural Networks—0
Towards Learning to Speak and Hear Through Multi-Agent Communication over a Continuous Acoustic Channel—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