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 1011–1020 of 1718 papers

Title	Date	Tasks	Status
Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNet	Dec 15, 2022	Deep Reinforcement LearningMulti-agent Reinforcement Learning	CodeCode Available
Multi-Agent Reinforcement Learning with Shared Resources for Inventory Management	Dec 15, 2022	ManagementMulti-agent Reinforcement Learning	—Unverified
Hierarchical Strategies for Cooperative Multi-Agent Reinforcement Learning	Dec 14, 2022	Graph AttentionMulti-agent Reinforcement Learning	—Unverified
Enabling the Wireless Metaverse via Semantic Multiverse Communication	Dec 13, 2022	Multi-agent Reinforcement LearningSemantic Communication	—Unverified
Scalable and Sample Efficient Distributed Policy Gradient Algorithms in Multi-Agent Networked Systems	Dec 13, 2022	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified
Effects of Spectral Normalization in Multi-agent Reinforcement Learning	Dec 10, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?	Dec 6, 2022	Deep Reinforcement LearningMulti-agent Reinforcement Learning	CodeCode Available
CURO: Curriculum Learning for Relative Overgeneralization	Dec 6, 2022	Efficient ExplorationMulti-agent Reinforcement Learning	—Unverified
E-MAPP: Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance	Dec 5, 2022	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified
DIAMOND: Taming Sample and Communication Complexities in Decentralized Bilevel Optimization	Dec 5, 2022	Bilevel OptimizationMeta-Learning	—Unverified

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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