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 1671–1680 of 1718 papers

Title	Date	Tasks	Status
Generalising Multi-Agent Cooperation through Task-Agnostic Communication	Mar 11, 2024	Multi-agent Reinforcement Learning	CodeCode Available
Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential Game	Jun 3, 2022	FairnessManagement	CodeCode Available
Scaling Laws for a Multi-Agent Reinforcement Learning Model	Sep 29, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
DistSPECTRL: Distributing Specifications in Multi-Agent Reinforcement Learning Systems	Jun 28, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
An Organizationally-Oriented Approach to Enhancing Explainability and Control in Multi-Agent Reinforcement Learning	Mar 30, 2025	Multi-agent Reinforcement Learning	CodeCode Available
QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement Learning	Dec 22, 2020	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
Learning Explicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning via Polarization Policy Gradient	Oct 10, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement Learning	Mar 16, 2023	Efficient ExplorationMulti-agent Reinforcement Learning	CodeCode Available
Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning	Feb 5, 2025	Adversarial AttackMulti-agent Reinforcement Learning	CodeCode Available
Towards Fault Tolerance in Multi-Agent Reinforcement Learning	Nov 30, 2024	Multi-agent Reinforcement Learningreinforcement-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