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 1621–1630 of 1718 papers

Title	Date	Tasks	Status
Convergent Policy Optimization for Safe Reinforcement Learning	Oct 26, 2019	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
Multi-agent reinforcement learning using echo-state network and its application to pedestrian dynamics	Dec 19, 2023	Multi-agent Reinforcement Learning	CodeCode Available
TinyQMIX: Distributed Access Control for mMTC via Multi-agent Reinforcement Learning	Nov 21, 2022	Deep Reinforcement LearningMulti-agent Reinforcement Learning	CodeCode Available
Learning to Communicate with Deep Multi-Agent Reinforcement Learning	May 21, 2016	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available
Prosocial learning agents solve generalized Stag Hunts better than selfish ones	Sep 8, 2017	Deep Reinforcement LearningMulti-agent Reinforcement Learning	CodeCode Available
Stateful active facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning	Oct 4, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
Smart Traffic Signals: Comparing MARL and Fixed-Time Strategies	May 20, 2025	Multi-agent Reinforcement Learning	CodeCode Available
Learning to Bid Long-Term: Multi-Agent Reinforcement Learning with Long-Term and Sparse Reward in Repeated Auction Games	Apr 5, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
Balancing Performance and Cost for Two-Hop Cooperative Communications: Stackelberg Game and Distributed Multi-Agent Reinforcement Learning	Jun 17, 2024	Multi-agent Reinforcement Learning	CodeCode Available
Efficient Collaborative Multi-Agent Deep Reinforcement Learning for Large-Scale Fleet Management	Feb 18, 2018	Deep Reinforcement LearningManagement	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