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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1631–1640 of 1718 papers

Title	Date	Tasks	Status
Whole-Chain Recommendations	Feb 11, 2019	Multi-agent Reinforcement LearningRecommendation Systems	—Unverified
Reinforcement Learning from Hierarchical Critics	Feb 8, 2019	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
Partner Selection for the Emergence of Cooperation in Multi-Agent Systems Using Reinforcement Learning	Feb 8, 2019	Multi-agent Reinforcement LearningReinforcement Learning	—Unverified
Decentralized Multi-Agents by Imitation of a Centralized Controller	Feb 6, 2019	Imitation LearningMulti-agent Reinforcement Learning	—Unverified
Learning to Schedule Communication in Multi-agent Reinforcement Learning	Feb 5, 2019	Deep Reinforcement LearningMulti-agent Reinforcement Learning	CodeCode Available
What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?	Feb 2, 2019	BIG-bench Machine LearningMulti-agent Reinforcement Learning	CodeCode Available
A Regulation Enforcement Solution for Multi-agent Reinforcement Learning	Jan 29, 2019	AI AgentManagement	—Unverified
Multi-Agent Reinforcement Learning with Multi-Step Generative Models	Jan 29, 2019	continuous-controlContinuous Control	—Unverified
Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning	Jan 27, 2019	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified
Modelling Bounded Rationality in Multi-Agent Interactions by Generalized Recursive Reasoning	Jan 26, 2019	Decision MakingMulti-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