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 301–310 of 1718 papers

Title	Date	Tasks	Status	Score
Solving Dynamic Principal-Agent Problems with a Rationally Inattentive Principal	Jan 18, 2022	Deep Reinforcement LearningMulti-agent Reinforcement Learning	CodeCode Available	5
Modelling crypto markets by multi-agent reinforcement learning	Feb 16, 2024	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	5
Certified Policy Smoothing for Cooperative Multi-Agent Reinforcement Learning	Dec 22, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	5
Mediated Multi-Agent Reinforcement Learning	Jun 14, 2023	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	5
Measuring Policy Distance for Multi-Agent Reinforcement Learning	Jan 20, 2024	DiversityMulti-agent Reinforcement Learning	CodeCode Available	5
RGMComm: Return Gap Minimization via Discrete Communications in Multi-Agent Reinforcement Learning	Aug 7, 2023	ClusteringMulti-agent Reinforcement Learning	CodeCode Available	5
Centralized Training with Hybrid Execution in Multi-Agent Reinforcement Learning	Oct 12, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	5
Centralized control for multi-agent RL in a complex Real-Time-Strategy game	Apr 25, 2023	Multi-agent Reinforcement Learning	CodeCode Available	5
Mean-Field Control based Approximation of Multi-Agent Reinforcement Learning in Presence of a Non-decomposable Shared Global State	Jan 13, 2023	Multi-agent Reinforcement Learning	CodeCode Available	5
MAVEN: Multi-Agent Variational Exploration	Oct 16, 2019	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