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 1426–1450 of 1718 papers

TitleStatusHype
Fairness-Oriented User Scheduling for Bursty Downlink Transmission Using Multi-Agent Reinforcement Learning—0
QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement LearningCode0
Difference Rewards Policy Gradients—0
MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning—0
Stabilizing Q Learning Via Soft Mellowmax Operator—0
Specializing Inter-Agent Communication in Heterogeneous Multi-Agent Reinforcement Learning using Agent Class Information—0
SAT-MARL: Specification Aware Training in Multi-Agent Reinforcement Learning—0
Flatland-RL : Multi-Agent Reinforcement Learning on Trains—0
Multi-agent Policy Optimization with Approximatively Synchronous Advantage Estimation—0
Fever Basketball: A Complex, Flexible, and Asynchronized Sports Game Environment for Multi-agent Reinforcement Learning—0
Multi-Objective Optimization of the Textile Manufacturing Process Using Deep-Q-Network Based Multi-Agent Reinforcement Learning—0
Robust Multi-Agent Reinforcement Learning with Model Uncertainty—0
Low-Bandwidth Communication Emerges Naturally in Multi-Agent Learning Systems—0
Minimax Sample Complexity for Turn-based Stochastic Game—0
PowerNet: Multi-agent Deep Reinforcement Learning for Scalable Powergrid Control—0
Multi-Agent Reinforcement Learning for Markov Routing Games: A New Modeling Paradigm For Dynamic Traffic Assignment—0
Multi-agent Reinforcement Learning Accelerated MCMC on Multiscale Inversion Problem—0
Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networks—0
Multi-Agent Reinforcement Learning for Channel Assignment and Power Allocation in Platoon-Based C-V2X Systems—0
Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial—0
LQR with Tracking: A Zeroth-order Approach and Its Global Convergence—0
Interpreting Graph Drawing with Multi-Agent Reinforcement Learning—0
Multi-Agent Reinforcement Learning for Visibility-based Persistent MonitoringCode0
Online Learning in Unknown Markov Games—0
On Information Asymmetry in Competitive Multi-Agent Reinforcement Learning: Convergence and Optimality—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