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 1041–1050 of 1718 papers

Title	Date	Tasks	Status	Hype
An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobility	Mar 15, 2022	Autonomous VehiclesMulti-agent Reinforcement Learning	—Unverified	0
The Multi-Agent Pickup and Delivery Problem: MAPF, MARL and Its Warehouse Applications	Mar 14, 2022	Multi-Agent Path FindingMulti-agent Reinforcement Learning	—Unverified	0
Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation	Mar 14, 2022	Autonomous DrivingGaussian Processes	—Unverified	0
Calibration of Derivative Pricing Models: a Multi-Agent Reinforcement Learning Perspective	Mar 14, 2022	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified	0
Impression Allocation and Policy Search in Display Advertising	Mar 11, 2022	Multi-agent Reinforcement Learning	—Unverified	0
Breaking the Curse of Dimensionality in Multiagent State Space: A Unified Agent Permutation Framework	Mar 10, 2022	Data AugmentationMulti-agent Reinforcement Learning	—Unverified	0
On-the-fly Strategy Adaptation for ad-hoc Agent Coordination	Mar 8, 2022	Game of HanabiMulti-agent Reinforcement Learning	—Unverified	0
Reliably Re-Acting to Partner's Actions with the Social Intrinsic Motivation of Transfer Empowerment	Mar 7, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1
Efficient Policy Generation in Multi-Agent Systems via Hypergraph Neural Network	Mar 7, 2022	Decision MakingDeep Reinforcement Learning	—Unverified	0
Scalable multi-agent reinforcement learning for distributed control of residential energy flexibility	Mar 7, 2022	Multi-agent Reinforcement LearningQ-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