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
The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis—0
The reinforcement learning-based multi-agent cooperative approach for the adaptive speed regulation on a metallurgical pickling line—0
The Synergy Between Optimal Transport Theory and Multi-Agent Reinforcement Learning—0
Toward Dependency Dynamics in Multi-Agent Reinforcement Learning for Traffic Signal Control—0
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
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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