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 461–470 of 1718 papers

Title	Date	Tasks	Status	Score
Generalising Multi-Agent Cooperation through Task-Agnostic Communication	Mar 11, 2024	Multi-agent Reinforcement Learning	CodeCode Available	5
DistSPECTRL: Distributing Specifications in Multi-Agent Reinforcement Learning Systems	Jun 28, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	5
Gifting in multi-agent reinforcement learning	May 5, 2020	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	5
Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement Learning	Mar 16, 2023	Efficient ExplorationMulti-agent Reinforcement Learning	CodeCode Available	5
Concurrent Meta Reinforcement Learning	Mar 7, 2019	Efficient ExplorationMeta-Learning	CodeCode Available	5
Finding Friend and Foe in Multi-Agent Games	Jun 5, 2019	counterfactualMulti-agent Reinforcement Learning	CodeCode Available	5
GOV-REK: Governed Reward Engineering Kernels for Designing Robust Multi-Agent Reinforcement Learning Systems	Apr 1, 2024	Multi-agent Reinforcement Learning	CodeCode Available	5
EXPODE: EXploiting POlicy Discrepancy for Efficient Exploration in Multi-agent Reinforcement Learning	May 30, 2023	Efficient ExplorationMulti-agent Reinforcement Learning	CodeCode Available	5
Arena: a toolkit for Multi-Agent Reinforcement Learning	Jul 20, 2019	Multi-agent Reinforcement LearningOpenAI Gym	CodeCode Available	5
Explainable Action Advising for Multi-Agent Reinforcement Learning	Nov 15, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	5

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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