Communities
Connect sessions
AI calendar
Organizations
Join Slack
Contact Sales

Terms and Conditions

Twitter GitHub LinkedIn Bluesky Youtube

© 2026 ResearchTrend.AI, All rights reserved.

Home
Papers
2402.05724
Cited By

Model-Based RL for Mean-Field Games is not Statistically Harder than
Single-Agent RL

v1v2 (latest)

Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL

8 February 2024

ArXiv (abs)PDF HTML

Papers citing "Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL"

7 / 7 papers shown

Policy Optimization for Continuous-time Linear-Quadratic Graphon Mean Field Games

Policy Optimization for Continuous-time Linear-Quadratic Graphon Mean Field Games

183

1

0

06 Jun 2025

Steering No-Regret Agents in MFGs under Model Uncertainty

Steering No-Regret Agents in MFGs under Model UncertaintyInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2025

283

2

0

12 Mar 2025

Decision-Dependent Stochastic Optimization: The Role of Distribution Dynamics

Florian Dorfler

Michael Muehlebach

255

5

0

10 Mar 2025

Exploiting Approximate Symmetry for Efficient Multi-Agent Reinforcement
Learning

Exploiting Approximate Symmetry for Efficient Multi-Agent Reinforcement LearningConference on Learning for Dynamics & Control (L4DC), 2024

230

5

0

27 Aug 2024

Learning to Steer Markovian Agents under Model Uncertainty

Learning to Steer Markovian Agents under Model Uncertainty

432

3

0

14 Jul 2024

MF-OML: Online Mean-Field Reinforcement Learning with Occupation Measures for Large Population Games

MF-OML: Online Mean-Field Reinforcement Learning with Occupation Measures for Large Population Games

287

7

0

01 May 2024

Networked Communication for Decentralised Agents in Mean-Field Games

Networked Communication for Decentralised Agents in Mean-Field Games

Patrick Benjamin

Alessandro Abate

469

2

0

05 Jun 2023

Page 1 of 1