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 1451–1460 of 1718 papers

Title	Date	Tasks	Status	Hype
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning	Jun 21, 2020	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified	0
Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning	Jun 21, 2020	FPS GamesGeneral Reinforcement Learning	CodeCode Available	1
Deep Implicit Coordination Graphs for Multi-agent Reinforcement Learning	Jun 19, 2020	Graph Neural NetworkMulti-agent Reinforcement Learning	CodeCode Available	1
Cooperative Multi-Agent Reinforcement Learning with Partial Observations	Jun 18, 2020	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified	0
Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning	Jun 18, 2020	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available	1
Efficient Ridesharing Dispatch Using Multi-Agent Reinforcement Learning	Jun 18, 2020	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	0
Distributed Value Function Approximation for Collaborative Multi-Agent Reinforcement Learning	Jun 18, 2020	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified	0
Eco-Vehicular Edge Networks for Connected Transportation: A Distributed Multi-Agent Reinforcement Learning Approach	Jun 17, 2020	Autonomous VehiclesMulti-agent Reinforcement Learning	—Unverified	0
Policy Evaluation and Seeking for Multi-Agent Reinforcement Learning via Best Response	Jun 17, 2020	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified	0
Multi-Agent Reinforcement Learning for Adaptive User Association in Dynamic mmWave Networks	Jun 16, 2020	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