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 31–40 of 1718 papers

Title	Date	Tasks	Status	Hype	Score
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
DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning	Jun 11, 2021	Card GamesDeep Reinforcement Learning	CodeCode Available	2	5
Digital Twin Vehicular Edge Computing Network: Task Offloading and Resource Allocation	Jul 16, 2024	Edge-computingMulti-agent Reinforcement Learning	CodeCode Available	2	5
Efficient Episodic Memory Utilization of Cooperative Multi-Agent Reinforcement Learning	Mar 2, 2024	DecoderMulti-agent Reinforcement Learning	CodeCode Available	2	5
Emergent Reciprocity and Team Formation from Randomized Uncertain Social Preferences	Nov 10, 2020	Multi-agent Reinforcement Learning	CodeCode Available	2	5
Heterogeneous-Agent Reinforcement Learning	Apr 19, 2023	LEMMAMulti-agent Reinforcement Learning	CodeCode Available	2	5
Heterogeneous Multi-Robot Reinforcement Learning	Jan 17, 2023	Graph Neural NetworkMulti-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
MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learning	Jul 23, 2024	BenchmarkingDecision Making	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