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 1691–1700 of 1718 papers

Title	Date	Tasks	Status	Hype
Learning to Gather without Communication	Feb 21, 2018	Multi-agent Reinforcement LearningPosition	CodeCode Available	0
Efficient Collaborative Multi-Agent Deep Reinforcement Learning for Large-Scale Fleet Management	Feb 18, 2018	Deep Reinforcement LearningManagement	CodeCode Available	0
Mean Field Multi-Agent Reinforcement Learning	Feb 15, 2018	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available	1
Cooperative Multi-Agent Reinforcement Learning for Low-Level Wireless Communication	Jan 14, 2018	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified	0
Neuron as an Agent	Jan 1, 2018	counterfactualMulti-agent Reinforcement Learning	—Unverified	0
MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence	Dec 2, 2017	GPUMulti-agent Reinforcement Learning	CodeCode Available	0
Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning	Nov 3, 2017	Multi-agent Reinforcement LearningMulti-Task Learning	—Unverified	0
Parameter Sharing Deep Deterministic Policy Gradient for Cooperative Multi-agent Reinforcement Learning	Oct 1, 2017	Deep Reinforcement LearningMulti-agent Reinforcement Learning	—Unverified	0
Learning with Opponent-Learning Awareness	Sep 13, 2017	Multi-agent Reinforcement LearningReinforcement Learning	CodeCode Available	0
Prosocial learning agents solve generalized Stag Hunts better than selfish ones	Sep 8, 2017	Deep Reinforcement LearningMulti-agent Reinforcement Learning	CodeCode Available	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