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 161–170 of 1718 papers

Title	Date	Tasks	Status	Hype
Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning	Jun 7, 2020	counterfactualMulti-agent Reinforcement Learning	CodeCode Available	1
Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Exploration	Jan 9, 2023	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1
Dashing for the Golden Snitch: Multi-Drone Time-Optimal Motion Planning with Multi-Agent Reinforcement Learning	Sep 25, 2024	Collision AvoidanceMotion Planning	CodeCode Available	1
Graph Convolutional Value Decomposition in Multi-Agent Reinforcement Learning	Oct 9, 2020	Deep Reinforcement LearningMulti-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
HAD-Gen: Human-like and Diverse Driving Behavior Modeling for Controllable Scenario Generation	Mar 19, 2025	Autonomous VehiclesImitation Learning	CodeCode Available	1
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
HyperMARL: Adaptive Hypernetworks for Multi-Agent RL	Dec 5, 2024	DiversityMulti-agent Reinforcement Learning	CodeCode Available	1
Hypothetical Minds: Scaffolding Theory of Mind for Multi-Agent Tasks with Large Language Models	Jul 9, 2024	Multi-agent Reinforcement Learning	CodeCode Available	1
Collaborating with Humans without Human Data	Oct 15, 2021	Multi-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