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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1601–1610 of 1718 papers

Title	Date	Tasks	Status	Hype
AgentGraph: Towards Universal Dialogue Management with Structured Deep Reinforcement Learning	May 27, 2019	Deep Reinforcement LearningDialogue Management	—Unverified	0
PAC Guarantees for Cooperative Multi-Agent Reinforcement Learning with Restricted Communication	May 23, 2019	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified	0
A Regularized Opponent Model with Maximum Entropy Objective	May 17, 2019	modelMulti-agent Reinforcement Learning	CodeCode Available	0
Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence	May 17, 2019	Multi-agent Reinforcement LearningReinforcement Learning	CodeCode Available	0
QBSO-FS: A Reinforcement Learning Based Bee Swarm Optimization Metaheuristic for Feature Selection	May 16, 2019	feature selectionMulti-agent Reinforcement Learning	CodeCode Available	0
QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning	May 14, 2019	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1
Deep Multi-Agent Reinforcement Learning Based Cooperative Edge Caching in Wireless Networks	May 13, 2019	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified	0
CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario	May 13, 2019	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	0
CoLight: Learning Network-level Cooperation for Traffic Signal Control	May 11, 2019	Multi-agent Reinforcement LearningReinforcement Learning	CodeCode Available	1
Emergent Escape-based Flocking Behavior using Multi-Agent Reinforcement Learning	May 10, 2019	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified	0

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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