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 151–160 of 1718 papers

Title	Date	Tasks	Status	Hype
AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N	Aug 15, 2022	EthicsMulti-agent Reinforcement Learning	CodeCode Available	1
Interaction Pattern Disentangling for Multi-Agent Reinforcement Learning	Jul 8, 2022	DiversityMulti-agent Reinforcement Learning	CodeCode Available	1
Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement Learning	Jul 5, 2022	DecoderMulti-agent Reinforcement Learning	CodeCode Available	1
The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward Functions	Jul 5, 2022	Multi-agent Reinforcement LearningSMAC+	CodeCode Available	1
Toward multi-target self-organizing pursuit in a partially observable Markov game	Jun 24, 2022	Decision MakingDeep Reinforcement Learning	CodeCode Available	1
MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer	Jun 20, 2022	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available	1
Stabilizing Voltage in Power Distribution Networks via Multi-Agent Reinforcement Learning with Transformer	Jun 8, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1
ALMA: Hierarchical Learning for Composite Multi-Agent Tasks	May 27, 2022	Decision MakingInductive Bias	CodeCode Available	1
Scalable Multi-Agent Model-Based Reinforcement Learning	May 25, 2022	Mambamodel	CodeCode Available	1
QGNN: Value Function Factorisation with Graph Neural Networks	May 25, 2022	Graph Neural NetworkMulti-agent Reinforcement Learning	CodeCode Available	1

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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