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.

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Papers

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Showing 1701–1710 of 1718 papers

Title	Date	Tasks	Status
A New Formalism, Method and Open Issues for Zero-Shot Coordination	Jun 11, 2021	Multi-agent Reinforcement Learning	CodeCode Available
FedMRL: Data Heterogeneity Aware Federated Multi-agent Deep Reinforcement Learning for Medical Imaging	Jul 8, 2024	Deep Reinforcement LearningFairness	CodeCode Available
Reinforcement Communication Learning in Different Social Network Structures	Jul 19, 2020	Multi-agent Reinforcement Learning	CodeCode Available
Learn How to Query from Unlabeled Data Streams in Federated Learning	Dec 11, 2024	BenchmarkingDecision Making	CodeCode Available
Towards Learning Transferable Conversational Skills using Multi-dimensional Dialogue Modelling	Mar 31, 2018	Dialogue ManagementDomain Adaptation	CodeCode Available
ColorGrid: A Multi-Agent Non-Stationary Environment for Goal Inference and Assistance	Jan 17, 2025	BenchmarkingMulti-agent Reinforcement Learning	CodeCode Available
Off-Policy Correction For Multi-Agent Reinforcement Learning	Nov 22, 2021	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
Last-Iterate Convergence with Full and Noisy Feedback in Two-Player Zero-Sum Games	Aug 21, 2022	Multi-agent Reinforcement Learning	CodeCode Available
VISER: A Tractable Solution Concept for Games with Information Asymmetry	Jul 18, 2023	Multi-agent Reinforcement Learning	CodeCode Available
Large-Scale Mixed-Traffic and Intersection Control using Multi-agent Reinforcement Learning	Apr 7, 2025	Autonomous DrivingMulti-agent Reinforcement Learning	CodeCode Available

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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