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 181–190 of 1718 papers

Title	Date	Tasks	Status	Hype	Score
Mava: a research library for distributed multi-agent reinforcement learning in JAX	Jul 3, 2021	Decision MakingMulti-agent Reinforcement Learning	CodeCode Available	1	5
Information Design in Multi-Agent Reinforcement Learning	May 8, 2023	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1	5
Rethinking the Implementation Matters in Cooperative Multi-Agent Reinforcement Learning	Feb 6, 2021	Multi-agent Reinforcement Learningreinforcement-learning	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
Effective and Stable Role-Based Multi-Agent Collaboration by Structural Information Principles	Apr 3, 2023	Multi-agent Reinforcement LearningStarcraft	CodeCode Available	1	5
Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT Networks	Jun 3, 2023	Federated LearningMulti-agent Reinforcement Learning	CodeCode Available	1	5
Hypothetical Minds: Scaffolding Theory of Mind for Multi-Agent Tasks with Large Language Models	Jul 9, 2024	Multi-agent Reinforcement Learning	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
Celebrating Diversity in Shared Multi-Agent Reinforcement Learning	Jun 4, 2021	DiversityMulti-agent Reinforcement Learning	CodeCode Available	1	5
HyperMARL: Adaptive Hypernetworks for Multi-Agent RL	Dec 5, 2024	DiversityMulti-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