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 1241–1250 of 1718 papers

Title	Date	Tasks	Status	Hype
Many Agent Reinforcement Learning Under Partial Observability	Jun 17, 2021	Deep Reinforcement LearningMulti-agent Reinforcement Learning	—Unverified	0
Cooperative Multi-Agent Reinforcement Learning Based Distributed Dynamic Spectrum Access in Cognitive Radio Networks	Jun 17, 2021	Multi-agent Reinforcement LearningReinforcement Learning (RL)	—Unverified	0
Minimizing Communication while Maximizing Performance in Multi-Agent Reinforcement Learning	Jun 15, 2021	Multi-agent Reinforcement LearningMulti-Task Learning	—Unverified	0
A Game-Theoretic Approach to Multi-Agent Trust Region Optimization	Jun 12, 2021	Atari GamesMuJoCo	CodeCode Available	1
A New Formalism, Method and Open Issues for Zero-Shot Coordination	Jun 11, 2021	Multi-agent Reinforcement Learning	CodeCode Available	0
A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising	Jun 11, 2021	Multi-agent Reinforcement Learning	CodeCode Available	1
DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning	Jun 11, 2021	Card GamesDeep Reinforcement Learning	CodeCode Available	2
Learning to Play General-Sum Games Against Multiple Boundedly Rational Agents	Jun 10, 2021	Decision MakingMulti-agent Reinforcement Learning	CodeCode Available	0
Metric Policy Representations for Opponent Modeling	Jun 10, 2021	Multi-agent Reinforcement Learning	—Unverified	0
Deception in Social Learning: A Multi-Agent Reinforcement Learning Perspective	Jun 9, 2021	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