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 1631–1640 of 1718 papers

Title	Date	Tasks	Status
What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?	Dec 6, 2022	Deep Reinforcement LearningMulti-agent Reinforcement Learning	CodeCode Available
Toward Finding Strong Pareto Optimal Policies in Multi-Agent Reinforcement Learning	Oct 25, 2024	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
Multi-Agent Reinforcement Learning with Action Masking for UAV-enabled Mobile Communications	Mar 29, 2023	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
A Regularized Opponent Model with Maximum Entropy Objective	May 17, 2019	modelMulti-agent Reinforcement Learning	CodeCode Available
A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning	Jun 4, 2023	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
Strangeness-driven Exploration in Multi-Agent Reinforcement Learning	Dec 27, 2022	Efficient ExplorationMulti-agent Reinforcement Learning	CodeCode Available
Context-Aware Bayesian Network Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning	Jun 2, 2023	Multi-agent Reinforcement Learning	CodeCode Available
Proximal Learning With Opponent-Learning Awareness	Oct 18, 2022	Multi-agent Reinforcement Learning	CodeCode Available
Multi-Agent Reinforcement Learning with Focal Diversity Optimization	Feb 6, 2025	DiversityMulti-agent Reinforcement Learning	CodeCode Available
Toward Policy Explanations for Multi-Agent Reinforcement Learning	Apr 26, 2022	Autonomous DrivingDecision Making	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