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 241–250 of 1718 papers

Title	Date	Tasks	Status	Hype
Communicative Reinforcement Learning Agents for Landmark Detection in Brain Images	Aug 18, 2020	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1
The Emergence of Adversarial Communication in Multi-Agent Reinforcement Learning	Aug 6, 2020	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1
QPLEX: Duplex Dueling Multi-Agent Q-Learning	Aug 3, 2020	Decision MakingMulti-agent Reinforcement Learning	CodeCode Available	1
Multi-Step Reinforcement Learning for Single Image Super-Resolution	Jul 28, 2020	Image Super-ResolutionMulti-agent Reinforcement Learning	CodeCode Available	1
Value-Decomposition Multi-Agent Actor-Critics	Jul 24, 2020	Multi-agent Reinforcement LearningReinforcement Learning (RL)	CodeCode Available	1
Off-Policy Multi-Agent Decomposed Policy Gradients	Jul 24, 2020	Multi-agent Reinforcement LearningStarcraft	CodeCode Available	1
Battlesnake Challenge: A Multi-agent Reinforcement Learning Playground with Human-in-the-loop	Jul 20, 2020	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1
Learning Implicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning	Jul 6, 2020	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1
Reward Machines for Cooperative Multi-Agent Reinforcement Learning	Jul 3, 2020	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available	1
Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning	Jun 21, 2020	FPS GamesGeneral 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