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.

Source: Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports

Papers

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Showing 81–90 of 1718 papers

Title	Date	Tasks	Status	Hype	Score
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning	Feb 16, 2021	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available	1	5
Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam Search	May 19, 2022	Decision MakingImage Captioning	CodeCode Available	1	5
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning	Jun 7, 2021	Multi-agent Reinforcement LearningOffline RL	CodeCode Available	1	5
CAMMARL: Conformal Action Modeling in Multi Agent Reinforcement Learning	Jun 19, 2023	Conformal PredictionDecision Making	CodeCode Available	1	5
C-COMA: A CONTINUAL REINFORCEMENT LEARNING MODEL FOR DYNAMIC MULTIAGENT ENVIRONMENTS	Apr 5, 2021	Continual LearningMulti-agent Reinforcement Learning	CodeCode Available	1	5
ALMA: Hierarchical Learning for Composite Multi-Agent Tasks	May 27, 2022	Decision MakingInductive Bias	CodeCode Available	1	5
Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning	Jun 7, 2020	counterfactualMulti-agent Reinforcement Learning	CodeCode Available	1	5
Chasing Moving Targets with Online Self-Play Reinforcement Learning for Safer Language Models	Jun 9, 2025	Multi-agent Reinforcement LearningSafety Alignment	CodeCode Available	1	5
Battlesnake Challenge: A Multi-agent Reinforcement Learning Playground with Human-in-the-loop	Jul 20, 2020	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1	5
Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning	Nov 4, 2018	DecoderMulti-agent Reinforcement 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