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 191–200 of 1718 papers

Title	Date	Tasks	Status	Hype
Context-aware Communication for Multi-agent Reinforcement Learning	Dec 25, 2023	Multi-agent Reinforcement LearningQuantization	CodeCode Available	1
Communicative Reinforcement Learning Agents for Landmark Detection in Brain Images	Aug 18, 2020	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1
Mava: a research library for distributed multi-agent reinforcement learning in JAX	Jul 3, 2021	Decision MakingMulti-agent Reinforcement Learning	CodeCode Available	1
Contrastive Identity-Aware Learning for Multi-Agent Value Decomposition	Nov 23, 2022	Contrastive LearningDiversity	CodeCode Available	1
Collaborating with Humans without Human Data	Oct 15, 2021	Multi-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
A multi-agent reinforcement learning model of common-pool resource appropriation	Jul 20, 2017	Deep Reinforcement LearningMulti-agent Reinforcement Learning	CodeCode Available	1
Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT Networks	Jun 3, 2023	Federated LearningMulti-agent Reinforcement Learning	CodeCode Available	1
More Centralized Training, Still Decentralized Execution: Multi-Agent Conditional Policy Factorization	Sep 26, 2022	Multi-agent Reinforcement Learning	CodeCode Available	1
Collaborative Visual Navigation	Jul 2, 2021	Multi-agent Reinforcement LearningNavigate	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