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 121–130 of 1718 papers

Title	Date	Tasks	Status	Hype
Information Design in Multi-Agent Reinforcement Learning	May 8, 2023	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1
System Neural Diversity: Measuring Behavioral Heterogeneity in Multi-Agent Learning	May 3, 2023	DiversityMulti-agent Reinforcement Learning	CodeCode Available	1
STAS: Spatial-Temporal Return Decomposition for Multi-agent Reinforcement Learning	Apr 15, 2023	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1
Language Instructed Reinforcement Learning for Human-AI Coordination	Apr 13, 2023	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1
MARL-iDR: Multi-Agent Reinforcement Learning for Incentive-based Residential Demand Response	Apr 8, 2023	ManagementMulti-agent Reinforcement Learning	CodeCode Available	1
Effective control of two-dimensional Rayleigh--Bénard convection: invariant multi-agent reinforcement learning is all you need	Apr 5, 2023	AllDeep Reinforcement Learning	CodeCode Available	1
Effective and Stable Role-Based Multi-Agent Collaboration by Structural Information Principles	Apr 3, 2023	Multi-agent Reinforcement LearningStarcraft	CodeCode Available	1
marl-jax: Multi-Agent Reinforcement Leaning Framework	Mar 24, 2023	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1
PoseExaminer: Automated Testing of Out-of-Distribution Robustness in Human Pose and Shape Estimation	Mar 13, 2023	Multi-agent Reinforcement Learning	CodeCode Available	1
SOCIALGYM 2.0: Simulator for Multi-Agent Social Robot Navigation in Shared Human Spaces	Mar 9, 2023	Multi-agent Reinforcement LearningRobot Navigation	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