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 1561–1570 of 1718 papers

Title	Date	Tasks	Status
eQMARL: Entangled Quantum Multi-Agent Reinforcement Learning for Distributed Cooperation over Quantum Channels	May 24, 2024	Multi-agent Reinforcement Learning	CodeCode Available
Data sharing games	Jan 26, 2021	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
Curriculum learning for multilevel budgeted combinatorial problems	Jul 7, 2020	Combinatorial OptimizationMulti-agent Reinforcement Learning	CodeCode Available
A Distributed Approach to Autonomous Intersection Management via Multi-Agent Reinforcement Learning	May 14, 2024	Autonomous VehiclesManagement	CodeCode Available
The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) Competition	Jan 23, 2019	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL	Dec 1, 2021	Multi-agent Reinforcement Learning	CodeCode Available
Inferring Latent Temporal Sparse Coordination Graph for Multi-Agent Reinforcement Learning	Mar 28, 2024	Graph LearningMulti-agent Reinforcement Learning	CodeCode Available
MAC-PO: Multi-Agent Experience Replay via Collective Priority Optimization	Feb 21, 2023	Decision MakingMulti-agent Reinforcement Learning	CodeCode Available
Independent Learning in Constrained Markov Potential Games	Feb 27, 2024	Multi-agent Reinforcement Learning	CodeCode Available
Multi-Agent Reinforcement Learning for Visibility-based Persistent Monitoring	Nov 2, 2020	Graph AttentionMulti-agent Reinforcement Learning	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