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 1–10 of 1718 papers

TitleStatusHype
One Step is Enough: Multi-Agent Reinforcement Learning based on One-Step Policy Optimization for Order Dispatch on Ride-Sharing PlatformsCode0
A Learning Framework For Cooperative Collision Avoidance of UAV Swarms Leveraging Domain Knowledge—0
Artificial Generals Intelligence: Mastering Generals.io with Reinforcement Learning—0
SPIRAL: Self-Play on Zero-Sum Games Incentivizes Reasoning via Multi-Agent Multi-Turn Reinforcement LearningCode2
The Decrypto Benchmark for Multi-Agent Reasoning and Theory of MindCode1
Center of Gravity-Guided Focusing Influence Mechanism for Multi-Agent Reinforcement Learning—0
Learning Bilateral Team Formation in Cooperative Multi-Agent Reinforcement Learning—0
Transformer World Model for Sample Efficient Multi-Agent Reinforcement LearningCode0
Generalizable Agent Modeling for Agent Collaboration-Competition Adaptation with Multi-Retrieval and Dynamic GenerationCode0
Multi-Agent Reinforcement Learning for Autonomous Multi-Satellite Earth Observation: A Realistic Case Study—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