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.

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Papers

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Showing 201–210 of 1718 papers

Title	Date	Tasks	Status	Hype
Multi-Agent Constrained Policy Optimisation	Oct 6, 2021	MuJoCoMulti-agent Reinforcement Learning	CodeCode Available	1
Multi-Agent Path Finding with Prioritized Communication Learning	Feb 8, 2022	Multi-Agent Path FindingMulti-agent Reinforcement Learning	CodeCode Available	1
Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement	Nov 2, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1
Communicative Reinforcement Learning Agents for Landmark Detection in Brain Images	Aug 18, 2020	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1
Collaborative Visual Navigation	Jul 2, 2021	Multi-agent Reinforcement LearningNavigate	CodeCode Available	1
Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Coordination by Multi-Critic Policy Gradient Optimization	Dec 31, 2020	Collision AvoidanceManagement	CodeCode Available	1
Context-aware Communication for Multi-agent Reinforcement Learning	Dec 25, 2023	Multi-agent Reinforcement LearningQuantization	CodeCode Available	1
Celebrating Diversity in Shared Multi-Agent Reinforcement Learning	Jun 4, 2021	DiversityMulti-agent Reinforcement Learning	CodeCode Available	1
Multi-Agent Reinforcement Learning via Distributed MPC as a Function Approximator	Dec 8, 2023	Model Predictive ControlMulti-agent Reinforcement Learning	CodeCode Available	1
Cooperative Multi-Agent Reinforcement Learning with Sequential Credit Assignment	May 21, 2021	counterfactualMulti-agent Reinforcement Learning	CodeCode Available	1

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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