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 251–260 of 1718 papers

Title	Date	Tasks	Status	Hype
Deep Implicit Coordination Graphs for Multi-agent Reinforcement Learning	Jun 19, 2020	Graph Neural NetworkMulti-agent Reinforcement Learning	CodeCode Available	1
Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning	Jun 18, 2020	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available	1
Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning	Jun 12, 2020	Efficient ExplorationMulti-agent Reinforcement Learning	CodeCode Available	1
Learning Individually Inferred Communication for Multi-Agent Cooperation	Jun 11, 2020	Causal InferenceMulti-agent Reinforcement Learning	CodeCode Available	1
The Emergence of Individuality	Jun 10, 2020	Multi-agent Reinforcement Learning	CodeCode Available	1
Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning	Jun 7, 2020	counterfactualMulti-agent Reinforcement Learning	CodeCode Available	1
Learning to Model Opponent Learning	Jun 6, 2020	Decision Makingmodel	CodeCode Available	1
Delay-Aware Multi-Agent Reinforcement Learning for Cooperative and Competitive Environments	May 11, 2020	Autonomous VehiclesDeep Reinforcement Learning	CodeCode Available	1
MARLeME: A Multi-Agent Reinforcement Learning Model Extraction Library	Apr 16, 2020	Model extractionMulti-agent Reinforcement Learning	CodeCode Available	1
Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning	Mar 23, 2020	Multi-agent Reinforcement Learningreinforcement-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