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 641–650 of 1718 papers

Title	Date	Tasks	Status	Hype
SACHA: Soft Actor-Critic with Heuristic-Based Attention for Partially Observable Multi-Agent Path Finding	Jul 5, 2023	Multi-Agent Path FindingMulti-agent Reinforcement Learning	CodeCode Available	1
Emergent Resource Exchange and Tolerated Theft Behavior using Multi-Agent Reinforcement Learning	Jul 4, 2023	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified	0
Beyond Conservatism: Diffusion Policies in Offline Multi-agent Reinforcement Learning	Jul 4, 2023	Data AugmentationDiversity	—Unverified	0
Environmental effects on emergent strategy in micro-scale multi-agent reinforcement learning	Jul 3, 2023	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1
Enhancing the Robustness of QMIX against State-adversarial Attacks	Jul 3, 2023	Deep Reinforcement LearningMulti-agent Reinforcement Learning	—Unverified	0
Theory of Mind as Intrinsic Motivation for Multi-Agent Reinforcement Learning	Jul 3, 2023	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified	0
Decentralized Multi-Agent Reinforcement Learning with Global State Prediction	Jun 22, 2023	Deep Reinforcement LearningMulti-agent Reinforcement Learning	—Unverified	0
IMP-MARL: a Suite of Environments for Large-scale Infrastructure Management Planning via MARL	Jun 20, 2023	BenchmarkingManagement	CodeCode Available	1
Discovering Causality for Efficient Cooperation in Multi-Agent Environments	Jun 20, 2023	Causal DiscoveryMulti-agent Reinforcement Learning	CodeCode Available	0
Cooperative Multi-Agent Learning for Navigation via Structured State Abstraction	Jun 20, 2023	Multi-agent Reinforcement Learning	—Unverified	0

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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