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 1111–1120 of 1718 papers

Title	Date	Tasks	Status	Hype
Distributed Policy Gradient with Variance Reduction in Multi-Agent Reinforcement Learning	Nov 25, 2021	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified	0
Application of Multi-Agent Reinforcement Learning for Battery Management in Renewable Mini-Grids	Nov 24, 2021	ManagementMulti-agent Reinforcement Learning	—Unverified	0
Fixed Points in Cyber Space: Rethinking Optimal Evasion Attacks in the Age of AI-NIDS	Nov 23, 2021	Continual LearningMulti-agent Reinforcement Learning	—Unverified	0
Multi-lingual agents through multi-headed neural networks	Nov 22, 2021	Continual LearningMulti-agent Reinforcement Learning	CodeCode Available	0
Episodic Multi-agent Reinforcement Learning with Curiosity-Driven Exploration	Nov 22, 2021	Efficient ExplorationMulti-agent Reinforcement Learning	CodeCode Available	1
Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor Rectification	Nov 22, 2021	Continuous ControlMulti-agent Reinforcement Learning	CodeCode Available	1
Off-Policy Correction For Multi-Agent Reinforcement Learning	Nov 22, 2021	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	0
Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning	Nov 21, 2021	Deep Reinforcement Learningenergy trading	—Unverified	0
Calculus of Consent via MARL: Legitimating the Collaborative Governance Supplying Public Goods	Nov 20, 2021	Multi-agent Reinforcement Learning	—Unverified	0
Polymatrix Competitive Gradient Descent	Nov 16, 2021	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