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 1591–1600 of 1718 papers

Title	Date	Tasks	Status
Learning with Opponent-Learning Awareness	Sep 13, 2017	Multi-agent Reinforcement LearningReinforcement Learning	CodeCode Available
Hierarchical Cooperative Multi-Agent Reinforcement Learning with Skill Discovery	Dec 7, 2019	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available
Learning Transferable Cooperative Behavior in Multi-Agent Teams	Jun 4, 2019	Multi-agent Reinforcement LearningReinforcement Learning	CodeCode Available
Arena: a toolkit for Multi-Agent Reinforcement Learning	Jul 20, 2019	Multi-agent Reinforcement LearningOpenAI Gym	CodeCode Available
Heterogeneous Multi-agent Zero-Shot Coordination by Coevolution	Aug 9, 2022	Multi-agent Reinforcement Learning	CodeCode Available
TOP-Former: A Multi-Agent Transformer Approach for the Team Orienteering Problem	Nov 30, 2023	Combinatorial OptimizationMulti-agent Reinforcement Learning	CodeCode Available
Efficient Robustness Assessment via Adversarial Spatial-Temporal Focus on Videos	Jan 3, 2023	Action RecognitionAdversarial Robustness	CodeCode Available
Cooperative multi-agent reinforcement learning for high-dimensional nonequilibrium control	Nov 12, 2021	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
PPS-QMIX: Periodically Parameter Sharing for Accelerating Convergence of Multi-Agent Reinforcement Learning	Mar 5, 2024	Federated LearningMulti-agent Reinforcement Learning	CodeCode Available
Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence	May 17, 2019	Multi-agent Reinforcement LearningReinforcement Learning	CodeCode Available

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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