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 1481–1490 of 1718 papers

Title	Date	Tasks	Status
Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound	Jul 30, 2020	DiagnosticMulti-agent Reinforcement Learning	—Unverified
Compare and Select: Video Summarization with Multi-Agent Reinforcement Learning	Jul 29, 2020	Decision MakingMulti-agent Reinforcement Learning	—Unverified
Multi-agent Reinforcement Learning in Bayesian Stackelberg Markov Games for Adaptive Moving Target Defense	Jul 20, 2020	Multi-agent Reinforcement LearningQ-Learning	—Unverified
Reinforcement Communication Learning in Different Social Network Structures	Jul 19, 2020	Multi-agent Reinforcement Learning	CodeCode Available
MAPS: Multi-agent Reinforcement Learning-based Portfolio Management System	Jul 10, 2020	ManagementMulti-agent Reinforcement Learning	—Unverified
Curriculum learning for multilevel budgeted combinatorial problems	Jul 7, 2020	Combinatorial OptimizationMulti-agent Reinforcement Learning	CodeCode Available
Consensus Multi-Agent Reinforcement Learning for Volt-VAR Control in Power Distribution Networks	Jul 6, 2020	Deep Reinforcement LearningManagement	—Unverified
Decentralized Deep Reinforcement Learning for Network Level Traffic Signal Control	Jul 2, 2020	Deep Reinforcement LearningMulti-agent Reinforcement Learning	—Unverified
R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games	Jun 30, 2020	Bayesian OptimizationMulti-agent Reinforcement Learning	—Unverified
Online Multi-agent Reinforcement Learning for Decentralized Inverter-based Volt-VAR Control	Jun 23, 2020	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified

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All datasets ParticleEnvs Cooperative Communication SMAC-Exp UAV Logistics

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	MATD3	final agent reward	-14	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DRIMA	Median Win Rate	15	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Fusion-Multi-Actor-Attention-Critic	Average Reward	39	—	Unverified