PDF Viewer

BUILDER'S SANDBOX

Build This Paper

Use an AI coding agent to implement this research.

OpenAI Codex
OpenAI CodexAI Agent

Lightweight coding agent in your terminal.

Claude Code
Claude CodeAI Agent

Agentic coding tool for terminal workflows.

AntiGravity IDE
AntiGravity IDEScaffolding

AI agent mindset installer and workflow scaffolder.

Cursor
CursorIDE

AI-first code editor built on VS Code.

VS Code
VS CodeIDE

Free, open-source editor by Microsoft.

Estimated $9K - $13K over 6-10 weeks.

See exactly what it costs to build this -- with 3 comparable funded startups.

7-day free trial. Cancel anytime.

Discover the researchers behind this paper and find similar experts.

7-day free trial. Cancel anytime.

References (38)

[1]
Solving Continuous Mean Field Games: Deep Reinforcement Learning for Non-Stationary Dynamics
2025Lorenzo Magnino, Kai Shao et al.
[2]
Regularization of the policy updates for stabilizing Mean Field Games
2023Talal Algumaei, Rubén Solozabal et al.
[3]
Towards a Standardised Performance Evaluation Protocol for Cooperative MARL
2022R. Gorsane, Omayma Mahjoub et al.
[4]
Learning in Mean Field Games: A Survey
2022M. Laurière, Sarah Perrin et al.
[5]
Scaling Mean Field Games by Online Mirror Descent
2022J. Pérolat, Sarah Perrin et al.
[6]
Scalable Deep Reinforcement Learning Algorithms for Mean Field Games
2022M. Laurière, Sarah Perrin et al.
[7]
Modeling Presymptomatic Spread in Epidemics via Mean-Field Games
2021S. Y. Olmez, Shubham Aggarwal et al.
[8]
Learning Equilibria in Mean-Field Games: Introducing Mean-Field PSRO
2021Paul Muller, Mark Rowland et al.
[9]
Concave Utility Reinforcement Learning: the Mean-field Game viewpoint
2021M. Geist, Julien P'erolat et al.
[10]
Mean Field Games Flock! The Reinforcement Learning Way
2021Sarah Perrin, M. Laurière et al.
[11]
Approximately Solving Mean Field Games via Entropy-Regularized Deep Reinforcement Learning
2021Kai Cui, H. Koeppl
[12]
Optimal Incentives to Mitigate Epidemics: A Stackelberg Mean Field Game Approach
2020Alexander Aurell, R. Carmona et al.
[13]
Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications
2020Sarah Perrin, J. Pérolat et al.
[14]
Unified reinforcement Q-learning for mean field game and control problems
2020Andrea Angiuli, J. Fouque et al.
[15]
Mean Field Games and Applications: Numerical Aspects
2020Y. Achdou, M. Laurière
[16]
Online Appendix: Numerical Methods for “Income and Wealth Distribution in Macroeconomics: A Continuous-Time Approach”
2020Y. Achdou, Jiequn Han et al.
[17]
Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms
2019K. Zhang, Zhuoran Yang et al.
[18]
Actor-Critic Provably Finds Nash Equilibria of Linear-Quadratic Mean-Field Games
2019Zuyue Fu, Zhuoran Yang et al.
[19]
OpenSpiel: A Framework for Reinforcement Learning in Games
2019Marc Lanctot, Edward Lockhart et al.
[20]
Reinforcement Learning in Stationary Mean-field Games
2019Jayakumar Subramanian, Aditya Mahajan

Showing 20 of 38 references

Founder's Pitch

"Bench-MFG offers a standardized benchmarking suite for evaluating learning algorithms in Mean Field Games, facilitating robust and comparable multi-agent system research."

BenchmarkingScore: 5View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

1/4 signals

2.5

Quick Build

2/4 signals

5

Series A Potential

2/4 signals

5

Sources used for this analysis

arXiv Paper

Full-text PDF analysis of the research paper

GitHub Repository

Code availability, stars, and contributor activity

Citation Network

Semantic Scholar citations and co-citation patterns

Community Predictions

Crowd-sourced unicorn probability assessments

Analysis model: GPT-4o · Last scored: 2/13/2026

Explore the full citation network and related research.

7-day free trial. Cancel anytime.

Understand the commercial significance and market impact.

7-day free trial. Cancel anytime.

Get detailed profiles of the research team.

7-day free trial. Cancel anytime.