I plan on using reinforcement learning for a research project. To be specific, I plan to define learning environments using market microstructure models whose solutions are well known and see if I can recover some attributes of equilibrium solutions using reinforcement learning.
I already went through the Q-Learning (and Deep-Q Learning) and Deep-Learning series available for free at https://pythonprogramming.net/. For fun, I wrote a script that discretizes Kyle's single auction model and teaches an agent how to behave like the Kyle insider using Q learning and an epsilon-greedy algorithm. The preliminary results seem encouraging, but I'd like to have more examples of codes and problems to get a better feel for it so I can eventually work on more complicated cases.
So, if you have references where I could find problems in finance like optimal trade execution, portfolio selection, etc. where reinforcement learning is used, I would greatly appreciate it. Ideally, this would come with the code in python as my goal would be to get better acquainted with how people tackle these problems. Note that I am also fairly familiar with R and MATLAB, if ever. I ask the question specifically here because I would really like these problems to be financial problems and I suspect at least some users of quant.stackexchange worked on these things. It's also because people who study or work in quantitative finance are likelier to know what sorts of financial problems can be viewed as games -- it's really the field-specific eample that I'm looking for.