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 381–390 of 1718 papers

TitleStatusHype
Predicting Multi-Agent Specialization via Task Parallelizability—0
PEnGUiN: Partially Equivariant Graph NeUral Networks for Sample Efficient MARL—0
Towards Better Sample Efficiency in Multi-Agent Reinforcement Learning via Exploration—0
A Comprehensive Survey on Multi-Agent Cooperative Decision-Making: Scenarios, Approaches, Challenges and Perspectives—0
A Generalist Hanabi AgentCode0
LLM-Mediated Guidance of MARL Systems—0
ICCO: Learning an Instruction-conditioned Coordinator for Language-guided Task-aligned Multi-robot Control—0
Unicorn: A Universal and Collaborative Reinforcement Learning Approach Towards Generalizable Network-Wide Traffic Signal Control—0
H2-MARL: Multi-Agent Reinforcement Learning for Pareto Optimality in Hospital Capacity Strain and Human Mobility during Epidemic—0
Enhancing Multi-Agent Systems via Reinforcement Learning with LLM-based Planner and Graph-based Policy—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