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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 1001–1025 of 1718 papers

TitleStatusHype
Optimization of Image Transmission in a Cooperative Semantic Communication Networks—0
Asynchronous Hybrid Reinforcement Learning for Latency and Reliability Optimization in the Metaverse over Wireless Communications—0
Decentralized Voltage Control with Peer-to-peer Energy Trading in a Distribution Network—0
Learning Individual Policies in Large Multi-agent Systems through Local Variance Minimization—0
Strangeness-driven Exploration in Multi-Agent Reinforcement LearningCode0
Coordinated Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Swarms in Autonomous Mobile Access Applications—0
Certified Policy Smoothing for Cooperative Multi-Agent Reinforcement LearningCode0
AdverSAR: Adversarial Search and Rescue via Multi-Agent Reinforcement Learning—0
Bandit approach to conflict-free multi-agent Q-learning in view of photonic implementation—0
An Energy-aware and Fault-tolerant Deep Reinforcement Learning based approach for Multi-agent Patrolling Problems—0
Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNetCode0
Multi-Agent Reinforcement Learning with Shared Resources for Inventory Management—0
Hierarchical Strategies for Cooperative Multi-Agent Reinforcement Learning—0
Enabling the Wireless Metaverse via Semantic Multiverse Communication—0
Scalable and Sample Efficient Distributed Policy Gradient Algorithms in Multi-Agent Networked Systems—0
Effects of Spectral Normalization in Multi-agent Reinforcement LearningCode0
What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?Code0
CURO: Curriculum Learning for Relative Overgeneralization—0
E-MAPP: Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance—0
DIAMOND: Taming Sample and Communication Complexities in Decentralized Bilevel Optimization—0
DACOM: Learning Delay-Aware Communication for Multi-Agent Reinforcement Learning—0
A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems—0
Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning—0
Multi-robot Social-aware Cooperative Planning in Pedestrian Environments Using Multi-agent Reinforcement Learning—0
Multi-agent reinforcement learning for wall modeling in LES of flow over periodic hills—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