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 1011–1020 of 1718 papers

TitleStatusHype
Conversational AI for Positive-sum Retailing under Falsehood ControlCode0
Using Fuzzy Logic to Learn Abstract Policies in Large-Scale Multi-Agent Reinforcement LearningCode0
Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication MethodCode1
Toward Policy Explanations for Multi-Agent Reinforcement LearningCode0
PP-MARL: Efficient Privacy-Preserving Multi-Agent Reinforcement Learning for Cooperative Intelligence in Communications—0
Collaborative Auto-Curricula Multi-Agent Reinforcement Learning with Graph Neural Network Communication Layer for Open-ended Wildfire-Management Resource Distribution—0
Resilient robot teams: a review integrating decentralised control, change-detection, and learning—0
Exact Formulas for Finite-Time Estimation Errors of Decentralized Temporal Difference Learning with Linear Function Approximation—0
Mingling Foresight with Imagination: Model-Based Cooperative Multi-Agent Reinforcement Learning—0
Federated Learning for Distributed Energy-Efficient Resource Allocation—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