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 1511–1520 of 1718 papers

Title	Date	Tasks	Status
MolOpt: Autonomous Molecular Geometry Optimization using Multi-Agent Reinforcement Learning	Aug 24, 2023	3D geometryComputational chemistry	CodeCode Available
Mediated Multi-Agent Reinforcement Learning	Jun 14, 2023	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
Large Legislative Models: Towards Efficient AI Policymaking in Economic Simulations	Oct 10, 2024	Decision MakingMulti-agent Reinforcement Learning	CodeCode Available
Measuring Policy Distance for Multi-Agent Reinforcement Learning	Jan 20, 2024	DiversityMulti-agent Reinforcement Learning	CodeCode Available
Mean-Field Control based Approximation of Multi-Agent Reinforcement Learning in Presence of a Non-decomposable Shared Global State	Jan 13, 2023	Multi-agent Reinforcement Learning	CodeCode Available
Deep Multi-Agent Reinforcement Learning with Relevance Graphs	Nov 30, 2018	Deep Reinforcement LearningMulti-agent Reinforcement Learning	CodeCode Available
Iterated Reasoning with Mutual Information in Cooperative and Byzantine Decentralized Teaming	Jan 20, 2022	Decision MakingMulti-agent Reinforcement Learning	CodeCode Available
MDPGT: Momentum-based Decentralized Policy Gradient Tracking	Dec 6, 2021	Multi-agent Reinforcement LearningPolicy Gradient Methods	CodeCode Available
Shapley Q-value: A Local Reward Approach to Solve Global Reward Games	Jul 11, 2019	Multi-agent Reinforcement LearningPolicy Gradient Methods	CodeCode Available
Towards Robust Multi-UAV Collaboration: MARL with Noise-Resilient Communication and Attention Mechanisms	Mar 4, 2025	counterfactualDecision Making	CodeCode Available

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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