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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 601–625 of 1718 papers

TitleStatusHype
Distributed Noncoherent Joint Transmission Based on Multi-Agent Reinforcement Learning for Dense Small Cell MISO Systems—0
Distributed multi-agent target search and tracking with Gaussian process and reinforcement learning—0
Carbon Footprint Reduction for Sustainable Data Centers in Real-Time—0
Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics—0
Distributed Learning Meets 6G: A Communication and Computing Perspective—0
Breaking the Curse of Multiagency in Robust Multi-Agent Reinforcement Learning—0
Distributed Learning in Wireless Networks: Recent Progress and Future Challenges—0
Distributed Deep Reinforcement Learning for Functional Split Control in Energy Harvesting Virtualized Small Cells—0
Distributed Cooperative Multi-Agent Reinforcement Learning with Directed Coordination Graph—0
Distributed Autonomous Swarm Formation for Dynamic Network Bridging—0
Can Sophisticated Dispatching Strategy Acquired by Reinforcement Learning? - A Case Study in Dynamic Courier Dispatching System—0
Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning—0
Discrete-Time Mean Field Control with Environment States—0
Discovering Individual Rewards in Collective Behavior through Inverse Multi-Agent Reinforcement Learning—0
Calibration of Derivative Pricing Models: a Multi-Agent Reinforcement Learning Perspective—0
Directly Attention Loss Adjusted Prioritized Experience Replay—0
Dimension-Free Rates for Natural Policy Gradient in Multi-Agent Reinforcement Learning—0
Calculus of Consent via MARL: Legitimating the Collaborative Governance Supplying Public Goods—0
A Neuro-Symbolic Approach to Multi-Agent RL for Interpretability and Probabilistic Decision Making—0
Advancing Multi-Organ Disease Care: A Hierarchical Multi-Agent Reinforcement Learning Framework—0
Calculus of Consent via MARL: Legitimating the Collaborative Governance Supplying Public Goods—0
Diffusion Models for Offline Multi-agent Reinforcement Learning with Safety Constraints—0
Diffusion-based Episodes Augmentation for Offline Multi-Agent Reinforcement Learning—0
CAFEEN: A Cooperative Approach for Energy Efficient NoCs with Multi-Agent Reinforcement Learning—0
An Efficient Distributed Multi-Agent Reinforcement Learning for EV Charging Network Control—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified