1 Introduction
1.1 Mean field games
1.2 Learning
1.3 Outline of the survey

2 Definition of the problems
2.1 Static MFG
2.2 Background on MDPs
2.3 Dynamic MFG setting with evolving mean field
2.4 Dynamic MFG settings with stationary mean field
2.5 Other solution concepts
2.6 Extensions

3 Iterative methods
3.1 Overview of the methods
3.2 Solving standard MDPs
3.3 Solving MFGs
3.4 Convergence and variants
3.5 Solving mean field social optimum

4 Reinforcement learning algorithms
4.1 Background on reinforcement learning
4.2 Reinforcement learning for MFGs
4.3 Reinforcement learning for Mean Field Control and MFMDP

5 Numerical experiments
5.1 Metrics
5.2 Algorithms
5.3 MFG Model

6 Conclusion and perspectives