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 81–90 of 1718 papers

Title	Date	Tasks	Status	Hype
Cooperation and Fairness in Multi-Agent Reinforcement Learning	Oct 19, 2024	FairnessMulti-agent Reinforcement Learning	CodeCode Available	1
Cooperative Policy Learning with Pre-trained Heterogeneous Observation Representations	Dec 24, 2020	DecoderGraph Attention	CodeCode Available	1
Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement Learning	May 28, 2019	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1
Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement Learning	Sep 22, 2023	counterfactualMulti-agent Reinforcement Learning	CodeCode Available	1
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement Learning	Mar 16, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1
ALMA: Hierarchical Learning for Composite Multi-Agent Tasks	May 27, 2022	Decision MakingInductive Bias	CodeCode Available	1
Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning	Jun 7, 2020	counterfactualMulti-agent Reinforcement Learning	CodeCode Available	1
Contrastive Identity-Aware Learning for Multi-Agent Value Decomposition	Nov 23, 2022	Contrastive LearningDiversity	CodeCode Available	1
A MARL Based Multi-Target Tracking Algorithm Under Jamming Against Radar	Dec 17, 2024	Multi-agent Reinforcement Learning	CodeCode Available	1
Controlling Behavioral Diversity in Multi-Agent Reinforcement Learning	May 23, 2024	DiversityMulti-agent 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