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 1351–1375 of 1718 papers

TitleStatusHype
Communication in Multi-Agent Reinforcement Learning: Intention Sharing—0
FSV: Learning to Factorize Soft Value Function for Cooperative Multi-Agent Reinforcement Learning—0
Learning to communicate through imagination with model-based deep multi-agent reinforcement learning—0
Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Coordination by Multi-Critic Policy Gradient OptimizationCode1
Fairness-Oriented User Scheduling for Bursty Downlink Transmission Using Multi-Agent Reinforcement Learning—0
Cooperative Policy Learning with Pre-trained Heterogeneous Observation RepresentationsCode1
QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement LearningCode0
Difference Rewards Policy Gradients—0
CityLearn: Standardizing Research in Multi-Agent Reinforcement Learning for Demand Response and Urban Energy ManagementCode1
MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning—0
Stabilizing Q Learning Via Soft Mellowmax Operator—0
Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement LearningCode1
SAT-MARL: Specification Aware Training in Multi-Agent Reinforcement Learning—0
Specializing Inter-Agent Communication in Heterogeneous Multi-Agent Reinforcement Learning using Agent Class Information—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
Learning Multi-Agent Communication through Structured Attentive ReasoningCode1
Low-Bandwidth Communication Emerges Naturally in Multi-Agent Learning Systems—0
Minimax Sample Complexity for Turn-based Stochastic Game—0
TLeague: A Framework for Competitive Self-Play based Distributed Multi-Agent Reinforcement LearningCode1
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
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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