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 1501–1525 of 1718 papers

TitleStatusHype
DeepSafeMPC: Deep Learning-Based Model Predictive Control for Safe Multi-Agent Reinforcement Learning—0
Demand-Aware Beam Hopping and Power Allocation for Load Balancing in Digital Twin empowered LEO Satellite Networks—0
Density-Aware Reinforcement Learning to Optimise Energy Efficiency in UAV-Assisted Networks—0
Dependent Multi-Task Learning with Causal Intervention for Image Captioning—0
Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity—0
Dialogue Management based on Multi-domain Corpus—0
DIAMOND: Taming Sample and Communication Complexities in Decentralized Bilevel Optimization—0
Difference Rewards Policy Gradients—0
Differentiable Arbitrating in Zero-sum Markov Games—0
Differentially Private Reinforcement Learning with Self-Play—0
Diffusion-based Episodes Augmentation for Offline Multi-Agent Reinforcement Learning—0
Diffusion Models for Offline Multi-agent Reinforcement Learning with Safety Constraints—0
Dimension-Free Rates for Natural Policy Gradient in Multi-Agent Reinforcement Learning—0
Directly Attention Loss Adjusted Prioritized Experience Replay—0
Discovering Individual Rewards in Collective Behavior through Inverse Multi-Agent Reinforcement Learning—0
Discrete-Time Mean Field Control with Environment States—0
Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning—0
Distributed Autonomous Swarm Formation for Dynamic Network Bridging—0
Distributed Cooperative Multi-Agent Reinforcement Learning with Directed Coordination Graph—0
Distributed Deep Reinforcement Learning for Functional Split Control in Energy Harvesting Virtualized Small Cells—0
Distributed Learning in Wireless Networks: Recent Progress and Future Challenges—0
Distributed Learning Meets 6G: A Communication and Computing Perspective—0
Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics—0
Distributed multi-agent target search and tracking with Gaussian process and reinforcement learning—0
Distributed Noncoherent Joint Transmission Based on Multi-Agent Reinforcement Learning for Dense Small Cell MISO Systems—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