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 791–800 of 1718 papers

Title	Date	Tasks	Status
Universal Agent Mixtures and the Geometry of Intelligence	Feb 13, 2023	Multi-agent Reinforcement LearningReinforcement Learning (RL)	—Unverified
Low Entropy Communication in Multi-Agent Reinforcement Learning	Feb 10, 2023	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified
Quantum Multi-Agent Actor-Critic Networks for Cooperative Mobile Access in Multi-UAV Systems	Feb 9, 2023	Multi-agent Reinforcement Learning	—Unverified
Learning Complex Teamwork Tasks Using a Given Sub-task Decomposition	Feb 9, 2023	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning	Feb 8, 2023	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified
Policy Evaluation in Decentralized POMDPs with Belief Sharing	Feb 8, 2023	Multi-agent Reinforcement Learning	CodeCode Available
Shared Information-Based Safe And Efficient Behavior Planning For Connected Autonomous Vehicles	Feb 8, 2023	Autonomous VehiclesMulti-agent Reinforcement Learning	—Unverified
Order book regulatory impact on stock market quality: a multi-agent reinforcement learning perspective	Feb 8, 2023	Multi-agent Reinforcement Learning	—Unverified
Learning Graph-Enhanced Commander-Executor for Multi-Agent Navigation	Feb 8, 2023	Hierarchical Reinforcement LearningMulti-agent Reinforcement Learning	CodeCode Available
Breaking the Curse of Multiagents in a Large State Space: RL in Markov Games with Independent Linear Function Approximation	Feb 7, 2023	Multi-agent Reinforcement 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