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 181–190 of 1718 papers

TitleStatusHype
BMG-Q: Localized Bipartite Match Graph Attention Q-Learning for Ride-Pooling Order Dispatch—0
WFCRL: A Multi-Agent Reinforcement Learning Benchmark for Wind Farm ControlCode1
An Offline Multi-Agent Reinforcement Learning Framework for Radio Resource Management—0
SRMT: Shared Memory for Multi-agent Lifelong PathfindingCode1
Tackling Uncertainties in Multi-Agent Reinforcement Learning through Integration of Agent Termination DynamicsCode0
Experience-replay Innovative Dynamics—0
ColorGrid: A Multi-Agent Non-Stationary Environment for Goal Inference and AssistanceCode0
ADAGE: A generic two-layer framework for adaptive agent based modelling—0
Networked Agents in the Dark: Team Value Learning under Partial Observability—0
AutoRestTest: A Tool for Automated REST API Testing Using LLMs and MARL—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