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 121–130 of 1718 papers

Title	Date	Tasks	Status	Hype
Game-Theoretic Multiagent Reinforcement Learning	Nov 1, 2020	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1
A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning	Oct 15, 2020	ManagementMulti-agent Reinforcement Learning	CodeCode Available	1
A Game-Theoretic Approach to Multi-Agent Trust Region Optimization	Jun 12, 2021	Atari GamesMuJoCo	CodeCode Available	1
Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learning	Dec 17, 2020	FairnessMulti-agent Reinforcement Learning	CodeCode Available	1
A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising	Jun 11, 2021	Multi-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
Controlling Behavioral Diversity in Multi-Agent Reinforcement Learning	May 23, 2024	DiversityMulti-agent Reinforcement Learning	CodeCode Available	1
Cooperative Policy Learning with Pre-trained Heterogeneous Observation Representations	Dec 24, 2020	DecoderGraph Attention	CodeCode Available	1
Collaborating with Humans without Human Data	Oct 15, 2021	Multi-agent Reinforcement Learning	CodeCode Available	1
CoLight: Learning Network-level Cooperation for Traffic Signal Control	May 11, 2019	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