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 951–960 of 1718 papers

Title	Date	Tasks	Status	Hype
Cooperative Actor-Critic via TD Error Aggregation	Jul 25, 2022	Multi-agent Reinforcement LearningReinforcement Learning (RL)	—Unverified	0
Few-Shot Teamwork	Jul 19, 2022	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified	0
Towards Global Optimality in Cooperative MARL with the Transformation And Distillation Framework	Jul 12, 2022	Multi-agent Reinforcement LearningPolicy Gradient Methods	—Unverified	0
Reward-Sharing Relational Networks in Multi-Agent Reinforcement Learning as a Framework for Emergent Behavior	Jul 12, 2022	Multi-agent Reinforcement LearningSociology	—Unverified	0
Interaction Pattern Disentangling for Multi-Agent Reinforcement Learning	Jul 8, 2022	DiversityMulti-agent Reinforcement Learning	CodeCode Available	1
High Performance Simulation for Scalable Multi-Agent Reinforcement Learning	Jul 8, 2022	GPUMulti-agent Reinforcement Learning	—Unverified	0
VMAS: A Vectorized Multi-Agent Simulator for Collective Robot Learning	Jul 7, 2022	BenchmarkingMulti-agent Reinforcement Learning	CodeCode Available	2
Decentralized scheduling through an adaptive, trading-based multi-agent system	Jul 5, 2022	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified	0
Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement Learning	Jul 5, 2022	DecoderMulti-agent Reinforcement Learning	CodeCode Available	1
The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward Functions	Jul 5, 2022	Multi-agent Reinforcement LearningSMAC+	CodeCode Available	1

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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