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 971–980 of 1718 papers

Title	Date	Tasks	Status	Hype
Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning	Jun 15, 2022	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified	0
Universally Expressive Communication in Multi-Agent Reinforcement Learning	Jun 14, 2022	Graph LearningMulti-agent Reinforcement Learning	CodeCode Available	0
Multi-Agent Neural Rewriter for Vehicle Routing with Limited Disclosure of Costs	Jun 13, 2022	Multi-agent Reinforcement Learning	—Unverified	0
Finite-Time Analysis of Fully Decentralized Single-Timescale Actor-Critic	Jun 12, 2022	Multi-agent Reinforcement LearningPrivacy Preserving	—Unverified	0
Deep Multi-Agent Reinforcement Learning with Hybrid Action Spaces based on Maximum Entropy	Jun 10, 2022	Deep Reinforcement LearningMulti-agent Reinforcement Learning	—Unverified	0
Stabilizing Voltage in Power Distribution Networks via Multi-Agent Reinforcement Learning with Transformer	Jun 8, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1
Scalable Joint Learning of Wireless Multiple-Access Policies and their Signaling	Jun 8, 2022	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified	0
Consensus Learning for Cooperative Multi-Agent Reinforcement Learning	Jun 6, 2022	Contrastive LearningMulti-agent Reinforcement Learning	—Unverified	0
Policy Optimization for Markov Games: Unified Framework and Faster Convergence	Jun 6, 2022	Multi-agent Reinforcement Learning	—Unverified	0
MACC: Cross-Layer Multi-Agent Congestion Control with Deep Reinforcement Learning	Jun 4, 2022	Deep Reinforcement LearningManagement	—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