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 371–380 of 1718 papers

Title	Date	Tasks	Status	Score
Learning Sparse Graphon Mean Field Games	Sep 8, 2022	Multi-agent Reinforcement Learning	CodeCode Available	5
Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi	Dec 9, 2024	Multi-agent Reinforcement Learning	CodeCode Available	5
Learning Graph-Enhanced Commander-Executor for Multi-Agent Navigation	Feb 8, 2023	Hierarchical Reinforcement LearningMulti-agent Reinforcement Learning	CodeCode Available	5
Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential Game	Jun 3, 2022	FairnessManagement	CodeCode Available	5
Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning	Jan 26, 2023	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available	5
Learning to Bid Long-Term: Multi-Agent Reinforcement Learning with Long-Term and Sparse Reward in Repeated Auction Games	Apr 5, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	5
Learn How to Query from Unlabeled Data Streams in Federated Learning	Dec 11, 2024	BenchmarkingDecision Making	CodeCode Available	5
Collaborative Information Dissemination with Graph-based Multi-Agent Reinforcement Learning	Aug 25, 2023	Autonomous VehiclesDisaster Response	CodeCode Available	5
Counterfactual Explanation with Multi-Agent Reinforcement Learning for Drug Target Prediction	Mar 24, 2021	counterfactualCounterfactual Explanation	CodeCode Available	5
Last-Iterate Convergence with Full and Noisy Feedback in Two-Player Zero-Sum Games	Aug 21, 2022	Multi-agent Reinforcement Learning	CodeCode Available	5

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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