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 121–130 of 1718 papers

Title	Date	Tasks	Status	Hype	Score
Game-Theoretic Multiagent Reinforcement Learning	Nov 1, 2020	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1	5
A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning	Oct 15, 2020	ManagementMulti-agent Reinforcement Learning	CodeCode Available	1	5
A Game-Theoretic Approach to Multi-Agent Trust Region Optimization	Jun 12, 2021	Atari GamesMuJoCo	CodeCode Available	1	5
AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N	Aug 15, 2022	EthicsMulti-agent Reinforcement Learning	CodeCode Available	1	5
CAMP: Collaborative Attention Model with Profiles for Vehicle Routing Problems	Jan 6, 2025	Computational EfficiencyMulti-agent Reinforcement Learning	CodeCode Available	1	5
Neural Auto-Curricula	Jun 4, 2021	Multi-agent Reinforcement Learning	CodeCode Available	1	5
Enhancing Cooperation through Selective Interaction and Long-term Experiences in Multi-Agent Reinforcement Learning	May 4, 2024	AttributeMulti-agent Reinforcement Learning	CodeCode Available	1	5
CAMMARL: Conformal Action Modeling in Multi Agent Reinforcement Learning	Jun 19, 2023	Conformal PredictionDecision Making	CodeCode Available	1	5
Enhancing Cooperative Multi-Agent Reinforcement Learning with State Modelling and Adversarial Exploration	May 8, 2025	Deep Reinforcement LearningMulti-agent Reinforcement Learning	CodeCode Available	1	5
Environmental effects on emergent strategy in micro-scale multi-agent reinforcement learning	Jul 3, 2023	Multi-agent Reinforcement Learningreinforcement-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