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 271–280 of 1718 papers

Title	Date	Tasks	Status	Hype
IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning	Oct 19, 2024	BenchmarkingMulti-agent Reinforcement Learning	CodeCode Available	2
Cooperation and Fairness in Multi-Agent Reinforcement Learning	Oct 19, 2024	FairnessMulti-agent Reinforcement Learning	CodeCode Available	1
Kaleidoscope: Learnable Masks for Heterogeneous Multi-agent Reinforcement Learning	Oct 11, 2024	DiversityMuJoCo	CodeCode Available	1
Large Legislative Models: Towards Efficient AI Policymaking in Economic Simulations	Oct 10, 2024	Decision MakingMulti-agent Reinforcement Learning	CodeCode Available	0
Learning to Balance Altruism and Self-interest Based on Empathy in Mixed-Motive Games	Oct 10, 2024	counterfactualCounterfactual Reasoning	—Unverified	0
Variational Inequality Methods for Multi-Agent Reinforcement Learning: Performance and Stability Gains	Oct 10, 2024	Multi-agent Reinforcement Learning	—Unverified	0
OPTIMA: Optimized Policy for Intelligent Multi-Agent Systems Enables Coordination-Aware Autonomous Vehicles	Oct 9, 2024	Autonomous VehiclesMulti-agent Reinforcement Learning	CodeCode Available	0
CAFEEN: A Cooperative Approach for Energy Efficient NoCs with Multi-Agent Reinforcement Learning	Oct 9, 2024	ManagementMulti-agent Reinforcement Learning	—Unverified	0
Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement Learning	Oct 8, 2024	GSM8KMulti-agent Reinforcement Learning	CodeCode Available	1
Cooperative and Asynchronous Transformer-based Mission Planning for Heterogeneous Teams of Mobile Robots	Oct 8, 2024	Decision MakingMulti-agent Reinforcement Learning	CodeCode Available	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