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 1621–1630 of 1718 papers

Title	Date	Tasks	Status	Hype
Distributed Power Control for Large Energy Harvesting Networks: A Multi-Agent Deep Reinforcement Learning Approach	Apr 1, 2019	Deep Reinforcement LearningMulti-agent Reinforcement Learning	—Unverified	0
Cooperative Multi-Agent Reinforcement Learning Framework for Scalping Trading	Mar 31, 2019	Deep Reinforcement LearningMulti-agent Reinforcement Learning	—Unverified	0
Failure-Scenario Maker for Rule-Based Agent using Multi-agent Adversarial Reinforcement Learning and its Application to Autonomous Driving	Mar 26, 2019	Autonomous DrivingMulti-agent Reinforcement Learning	—Unverified	0
Distributed off-Policy Actor-Critic Reinforcement Learning with Policy Consensus	Mar 21, 2019	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified	0
Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces	Mar 12, 2019	Deep Reinforcement LearningMulti-agent Reinforcement Learning	—Unverified	0
Concurrent Meta Reinforcement Learning	Mar 7, 2019	Efficient ExplorationMeta-Learning	CodeCode Available	0
Can Sophisticated Dispatching Strategy Acquired by Reinforcement Learning? - A Case Study in Dynamic Courier Dispatching System	Mar 7, 2019	Multi-agent Reinforcement LearningReinforcement Learning	—Unverified	0
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics Network	Mar 2, 2019	Combinatorial OptimizationManagement	CodeCode Available	1
Message-Dropout: An Efficient Training Method for Multi-Agent Deep Reinforcement Learning	Feb 18, 2019	Deep Reinforcement LearningMulti-agent Reinforcement Learning	—Unverified	0
The StarCraft Multi-Agent Challenge	Feb 11, 2019	BenchmarkingMuJoCo	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