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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 291–300 of 1718 papers

Title	Date	Tasks	Status	Score
Modeling Moral Choices in Social Dilemmas with Multi-Agent Reinforcement Learning	Jan 20, 2023	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	5
Modelling Opaque Bilateral Market Dynamics in Financial Trading: Insights from a Multi-Agent Simulation Study	May 5, 2024	Multi-agent Reinforcement Learning	CodeCode Available	5
Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed Agents	Mar 16, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	5
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning	Sep 13, 2018	Autonomous VehiclesEfficient Exploration	CodeCode Available	5
What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?	Feb 2, 2019	BIG-bench Machine LearningMulti-agent Reinforcement Learning	CodeCode Available	5
Solving Dynamic Principal-Agent Problems with a Rationally Inattentive Principal	Jan 18, 2022	Deep Reinforcement LearningMulti-agent Reinforcement Learning	CodeCode Available	5
MolOpt: Autonomous Molecular Geometry Optimization using Multi-Agent Reinforcement Learning	Aug 24, 2023	3D geometryComputational chemistry	CodeCode Available	5
CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario	May 13, 2019	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	5
An Organizationally-Oriented Approach to Enhancing Explainability and Control in Multi-Agent Reinforcement Learning	Mar 30, 2025	Multi-agent Reinforcement Learning	CodeCode Available	5
Measuring Policy Distance for Multi-Agent Reinforcement Learning	Jan 20, 2024	DiversityMulti-agent Reinforcement Learning	CodeCode Available	5

Show:10 25 50

← PrevPage 30 of 172Next →

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