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 221–230 of 1718 papers

Title	Date	Tasks	Status	Hype	Score
CityLearn: Standardizing Research in Multi-Agent Reinforcement Learning for Demand Response and Urban Energy Management	Dec 18, 2020	energy managementManagement	CodeCode Available	1	5
Reliably Re-Acting to Partner's Actions with the Social Intrinsic Motivation of Transfer Empowerment	Mar 7, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1	5
IMP-MARL: a Suite of Environments for Large-scale Infrastructure Management Planning via MARL	Jun 20, 2023	BenchmarkingManagement	CodeCode Available	1	5
Inequity aversion improves cooperation in intertemporal social dilemmas	Mar 23, 2018	Multi-agent Reinforcement LearningReinforcement Learning	CodeCode Available	1	5
Learning Implicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning	Jul 6, 2020	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1	5
Individual Contributions as Intrinsic Exploration Scaffolds for Multi-agent Reinforcement Learning	May 28, 2024	Multi-agent Reinforcement LearningSMAC	CodeCode Available	1	5
An Extended Benchmarking of Multi-Agent Reinforcement Learning Algorithms in Complex Fully Cooperative Tasks	Feb 7, 2025	BenchmarkingMulti-agent Reinforcement Learning	CodeCode Available	1	5
Information Design in Multi-Agent Reinforcement Learning	May 8, 2023	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1	5
C-COMA: A CONTINUAL REINFORCEMENT LEARNING MODEL FOR DYNAMIC MULTIAGENT ENVIRONMENTS	Apr 5, 2021	Continual LearningMulti-agent Reinforcement Learning	CodeCode Available	1	5
Learning to Control and Coordinate Mixed Traffic Through Robot Vehicles at Complex and Unsignalized Intersections	Jan 12, 2023	Autonomous VehiclesMulti-agent Reinforcement Learning	CodeCode Available	1	5

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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