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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 826–850 of 1718 papers

TitleStatusHype
Self-Motivated Multi-Agent ExplorationCode0
Scalable Communication for Multi-Agent Reinforcement Learning via Transformer-Based Email Mechanism—0
Efficient Robustness Assessment via Adversarial Spatial-Temporal Focus on VideosCode0
Temporal Difference Learning with Compressed Updates: Error-Feedback meets Reinforcement Learning—0
Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics—0
Large-Scale Traffic Signal Control by a Nash Deep Q-network Approach—0
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
Scalable Multi-Agent Reinforcement Learning for Warehouse Logistics with Robotic and Human Co-WorkersCode1
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
SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement LearningCode2
Hierarchical Strategies for Cooperative Multi-Agent Reinforcement Learning—0
Scalable and Sample Efficient Distributed Policy Gradient Algorithms in Multi-Agent Networked Systems—0
Enabling the Wireless Metaverse via Semantic Multiverse Communication—0
Effects of Spectral Normalization in Multi-agent Reinforcement LearningCode0
What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?Code0
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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