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 1491–1500 of 1718 papers

Title	Date	Tasks	Status
Reinforcement Learning Based Robust Volt/Var Control in Active Distribution Networks With Imprecisely Known Delay	Feb 27, 2024	Multi-agent Reinforcement Learning	—Unverified
Multi-agent Reinforcement Learning for Decentralized Stable Matching	May 3, 2020	FairnessMulti-agent Reinforcement Learning	—Unverified
What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?	Feb 2, 2019	BIG-bench Machine LearningMulti-agent Reinforcement Learning	CodeCode Available
Discovering Causality for Efficient Cooperation in Multi-Agent Environments	Jun 20, 2023	Causal DiscoveryMulti-agent Reinforcement Learning	CodeCode Available
RGMComm: Return Gap Minimization via Discrete Communications in Multi-Agent Reinforcement Learning	Aug 7, 2023	ClusteringMulti-agent Reinforcement Learning	CodeCode Available
Extended Markov Games to Learn Multiple Tasks in Multi-Agent Reinforcement Learning	Feb 14, 2020	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages	Jun 2, 2023	Bayesian Inferencecontinuous-control	CodeCode Available
EXPODE: EXploiting POlicy Discrepancy for Efficient Exploration in Multi-agent Reinforcement Learning	May 30, 2023	Efficient ExplorationMulti-agent Reinforcement Learning	CodeCode Available
Digital Twin-Based Multiple Access Optimization and Monitoring via Model-Driven Bayesian Learning	Oct 11, 2022	Anomaly DetectionMulti-agent Reinforcement Learning	CodeCode Available
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning	Sep 13, 2018	Autonomous VehiclesEfficient Exploration	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