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 1571–1580 of 1718 papers

Title	Date	Tasks	Status
Communicating via Markov Decision Processes	Jul 17, 2021	Multi-agent Reinforcement Learning	CodeCode Available
M^3RL: Mind-aware Multi-agent Management Reinforcement Learning	Sep 29, 2018	ManagementMulti-agent Reinforcement Learning	CodeCode Available
Logic-based Reward Shaping for Multi-Agent Reinforcement Learning	Jun 17, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
Adaptive Value Decomposition with Greedy Marginal Contribution Computation for Cooperative Multi-Agent Reinforcement Learning	Feb 14, 2023	Multi-agent Reinforcement Learning	CodeCode Available
Classifying Ambiguous Identities in Hidden-Role Stochastic Games with Multi-Agent Reinforcement Learning	Oct 24, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
PMAT: Optimizing Action Generation Order in Multi-Agent Reinforcement Learning	Feb 23, 2025	Action GenerationDecision Making	CodeCode Available
Homogeneous Learning: Self-Attention Decentralized Deep Learning	Oct 11, 2021	Deep LearningFederated Learning	CodeCode Available
Reinforcement Learning from Hierarchical Critics	Feb 8, 2019	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
Counterfactual Explanation with Multi-Agent Reinforcement Learning for Drug Target Prediction	Mar 24, 2021	counterfactualCounterfactual Explanation	CodeCode Available
Enhancing Language Multi-Agent Learning with Multi-Agent Credit Re-Assignment for Interactive Environment Generalization	Feb 20, 2025	Multi-agent Reinforcement 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