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 1551–1560 of 1718 papers

Title	Date	Tasks	Status
Revisiting Parameter Sharing in Multi-Agent Deep Reinforcement Learning	May 27, 2020	Deep Reinforcement LearningMulti-agent Reinforcement Learning	CodeCode Available
SMIX(λ): Enhancing Centralized Value Functions for Cooperative Multi-Agent Reinforcement Learning	Nov 11, 2019	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
Multi-Agent Quantum Reinforcement Learning using Evolutionary Optimization	Nov 9, 2023	Autonomous DrivingMulti-agent Reinforcement Learning	CodeCode Available
Interpretable Emergent Language Using Inter-Agent Transformers	May 4, 2025	Multi-agent Reinforcement Learning	CodeCode Available
Partially Observable Mean Field Multi-Agent Reinforcement Learning Based on Graph-Attention	Apr 25, 2023	Graph AttentionMulti-agent Reinforcement Learning	CodeCode Available
A collaboration of multi-agent model using an interactive interface	Sep 1, 2022	Multi-agent Reinforcement Learning	CodeCode Available
Information State Embedding in Partially Observable Cooperative Multi-Agent Reinforcement Learning	Apr 2, 2020	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
Enhancing Heterogeneous Multi-Agent Cooperation in Decentralized MARL via GNN-driven Intrinsic Rewards	Aug 12, 2024	Graph Neural NetworkMulti-agent Reinforcement Learning	CodeCode Available
RLCard: A Toolkit for Reinforcement Learning in Card Games	Oct 10, 2019	Board GamesCard Games	CodeCode Available
Multi-Agent Reinforcement Learning: A Report on Challenges and Approaches	Jul 25, 2018	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