1 Introduction
1.1 Overview: learning for games with continuous action space
1.2 Contribution
1.3 Structure

2 Mean field game model
2.1 Aggregate-based best-response
2.2 Banach-Picard learning algorithm
2.3 Faster algorithms: Banach-Picard vs Ishikawa
2.4 Reverse Ishikawa mean-field learning
2.5 Non-asymptotic properties
2.6 Asymptotic pseudo-trajectories

3 Mean-field learning
3.1 How to accelerate the convergence rate?
3.2 Fully distributed derivative-free mean field learning
3.3 Feedback-free mean-field learning

4 Beauty contest game

5 Speedup strategic learning for satisfactory solution

6 Concluding remarks