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 171–180 of 1718 papers

Title	Date	Tasks	Status	Hype
Learning to Share in Multi-Agent Reinforcement Learning	Dec 16, 2021	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1
Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Tasks	Dec 6, 2021	AllMulti-agent Reinforcement Learning	CodeCode Available	1
Regularized Softmax Deep Multi-Agent Q-Learning	Dec 1, 2021	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available	1
Neural Auto-Curricula in Two-Player Zero-Sum Games	Dec 1, 2021	Multi-agent Reinforcement LearningVocal Bursts Valence Prediction	CodeCode Available	1
VAST: Value Function Factorization with Variable Agent Sub-Teams	Dec 1, 2021	Multi-agent Reinforcement Learning	CodeCode Available	1
Episodic Multi-agent Reinforcement Learning with Curiosity-Driven Exploration	Nov 22, 2021	Efficient ExplorationMulti-agent Reinforcement Learning	CodeCode Available	1
Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor Rectification	Nov 22, 2021	Continuous ControlMulti-agent Reinforcement Learning	CodeCode Available	1
Resilient Consensus-based Multi-agent Reinforcement Learning with Function Approximation	Nov 12, 2021	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1
PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems	Nov 10, 2021	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1
Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems	Nov 8, 2021	Multi-agent Reinforcement Learning	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