Research map / Multi-agent systems and games / Games and equilibria
On the Convergence of Model Free Learning in Mean Field Games
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Bench-MFG: A Benchmark Suite for Learning in Stationary Mean Field Games (NeurIPS 2026 · similarity 0.91)
- Bounded Rationality Equilibrium Learning in Mean Field Games (AAAI 2025 · similarity 0.91)
- Stochastic Semi-Gradient Descent for Learning Mean Field Games with Population-Aware Function Approximation (ICLR 2025 · similarity 0.91)
- Solving Continuous Mean Field Games: Deep Reinforcement Learning for Non-Stationary Dynamics (NeurIPS 2025 · similarity 0.91)
- Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL (ICML 2024 · similarity 0.90)
- Learning Discrete-Time Major-Minor Mean Field Games (AAAI 2024 · similarity 0.90)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
