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 21–30 of 1718 papers

Title	Date	Tasks	Status	Hype	Score
ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-Dependency	Nov 29, 2022	Decision MakingMulti-agent Reinforcement Learning	CodeCode Available	2	5
Ensembling Prioritized Hybrid Policies for Multi-agent Pathfinding	Mar 12, 2024	Multi-Agent Path FindingMulti-agent Reinforcement Learning	CodeCode Available	2	5
Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement Learning	Jan 25, 2025	Answer GenerationMulti-agent Reinforcement Learning	CodeCode Available	2	5
IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning	Oct 19, 2024	BenchmarkingMulti-agent Reinforcement Learning	CodeCode Available	2	5
Deep Reinforcement Learning for Multi-Agent Interaction	Aug 2, 2022	BIG-bench Machine LearningCausal Inference	CodeCode Available	2	5
MAexp: A Generic Platform for RL-based Multi-Agent Exploration	Apr 19, 2024	DiversityMulti-agent Reinforcement Learning	CodeCode Available	2	5
MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning	Sep 26, 2021	BenchmarkingDecision Making	CodeCode Available	2	5
Mini Honor of Kings: A Lightweight Environment for Multi-Agent Reinforcement Learning	Jun 6, 2024	Multi-agent Reinforcement Learning	CodeCode Available	2	5
Coordinate-Aligned Multi-Camera Collaboration for Active Multi-Object Tracking	Feb 22, 2022	Multi-agent Reinforcement LearningMulti-Object Tracking	CodeCode Available	2	5
Developing A Multi-Agent and Self-Adaptive Framework with Deep Reinforcement Learning for Dynamic Portfolio Risk Management	Feb 1, 2024	Deep Reinforcement LearningManagement	CodeCode Available	2	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