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 651–660 of 1718 papers

Title	Date	Tasks	Status	Hype
CAMMARL: Conformal Action Modeling in Multi Agent Reinforcement Learning	Jun 19, 2023	Conformal PredictionDecision Making	CodeCode Available	1
Maximum Entropy Heterogeneous-Agent Reinforcement Learning	Jun 19, 2023	MuJoCoMulti-agent Reinforcement Learning	CodeCode Available	2
Dynamic Size Message Scheduling for Multi-Agent Communication under Limited Bandwidth	Jun 16, 2023	Multi-agent Reinforcement LearningScheduling	—Unverified	0
Offline Multi-Agent Reinforcement Learning with Coupled Value Factorization	Jun 15, 2023	ManagementMulti-agent Reinforcement Learning	—Unverified	0
Decentralized Social Navigation with Non-Cooperative Robots via Bi-Level Optimization	Jun 15, 2023	Collision AvoidanceMulti-agent Reinforcement Learning	CodeCode Available	1
Density-Aware Reinforcement Learning to Optimise Energy Efficiency in UAV-Assisted Networks	Jun 14, 2023	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified	0
Mediated Multi-Agent Reinforcement Learning	Jun 14, 2023	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	0
Hierarchical Task Network Planning for Facilitating Cooperative Multi-Agent Reinforcement Learning	Jun 14, 2023	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified	0
Data Poisoning to Fake a Nash Equilibrium in Markov Games	Jun 13, 2023	Data PoisoningMulti-agent Reinforcement Learning	—Unverified	0
Provably Learning Nash Policies in Constrained Markov Potential Games	Jun 13, 2023	Decision MakingMulti-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