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 21–30 of 1718 papers

Title	Date	Tasks	Status	Hype
MAexp: A Generic Platform for RL-based Multi-Agent Exploration	Apr 19, 2024	DiversityMulti-agent Reinforcement Learning	CodeCode Available	2
Ensembling Prioritized Hybrid Policies for Multi-agent Pathfinding	Mar 12, 2024	Multi-Agent Path FindingMulti-agent Reinforcement Learning	CodeCode Available	2
Efficient Episodic Memory Utilization of Cooperative Multi-Agent Reinforcement Learning	Mar 2, 2024	DecoderMulti-agent Reinforcement Learning	CodeCode Available	2
Developing A Multi-Agent and Self-Adaptive Framework with Deep Reinforcement Learning for Dynamic Portfolio Risk Management	Feb 1, 2024	Deep Reinforcement LearningManagement	CodeCode Available	2
Tactics2D: A Highly Modular and Extensible Simulator for Driving Decision-making	Nov 18, 2023	Autonomous DrivingDecision Making	CodeCode Available	2
JaxMARL: Multi-Agent RL Environments and Algorithms in JAX	Nov 16, 2023	CPUGPU	CodeCode Available	2
ZSC-Eval: An Evaluation Toolkit and Benchmark for Multi-agent Zero-shot Coordination	Oct 8, 2023	DiversityMulti-agent Reinforcement Learning	CodeCode Available	2
Maximum Entropy Heterogeneous-Agent Reinforcement Learning	Jun 19, 2023	MuJoCoMulti-agent Reinforcement Learning	CodeCode Available	2
Heterogeneous-Agent Reinforcement Learning	Apr 19, 2023	LEMMAMulti-agent Reinforcement Learning	CodeCode Available	2
Off-the-Grid MARL: Datasets with Baselines for Offline Multi-Agent Reinforcement Learning	Feb 1, 2023	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	2

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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