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.

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Papers

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Showing 1141–1150 of 1718 papers

Title	Date	Tasks	Status
Sparse Adversarial Attack in Multi-agent Reinforcement Learning	May 19, 2022	Adversarial AttackMulti-agent Reinforcement Learning	—Unverified
Distributed Transmission Control for Wireless Networks using Multi-Agent Reinforcement Learning	May 13, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
Emergent Bartering Behaviour in Multi-Agent Reinforcement Learning	May 13, 2022	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified
Efficient Distributed Framework for Collaborative Multi-Agent Reinforcement Learning	May 11, 2022	DiversityMulti-agent Reinforcement Learning	—Unverified
Multi-Target Active Object Tracking with Monte Carlo Tree Search and Target Motion Modeling	May 7, 2022	Multi-agent Reinforcement LearningObject Tracking	—Unverified
LDSA: Learning Dynamic Subtask Assignment in Cooperative Multi-Agent Reinforcement Learning	May 5, 2022	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified
General sum stochastic games with networked information flows	May 5, 2022	Decision MakingManagement	—Unverified
Conversational AI for Positive-sum Retailing under Falsehood Control	May 1, 2022	Decision MakingMulti-agent Reinforcement Learning	CodeCode Available
Using Fuzzy Logic to Learn Abstract Policies in Large-Scale Multi-Agent Reinforcement Learning	Apr 27, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
Toward Policy Explanations for Multi-Agent Reinforcement Learning	Apr 26, 2022	Autonomous DrivingDecision Making	CodeCode Available

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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