For a few months I’ve been anticipating the publication of Reinforcement Learning for Finance by Yves Hilpisch, supposedly in December, according to Amazon. However I found today that it is already available, and have purchased the Kindle edition. All the books I have on RL deal with non-financial problems, except for one by Ivan Gridin that has a chapter on RL in stock pricing, although it is a bit simplistic. So this text promises to fill a gap, which is exactly what Dr. Hilpisch said in his introduction.
I’m just about to get into it, and fortunately I have become a lot more comfortable with Tensorflow lately, and I expect that’s what he’ll be using. I don’t know how much time I’ll be able to devote to this at the moment – I have a few pressing matters to sort out over the next couple of months. However there’s no way I can not get started, at least. I’m hoping that all the study that I’ve already done on RL will make this book fairly easy to work through. Anyway, we’ll see.
Update:
Well, that was disappointing. I’ve reached Chapter 2, which starts to discuss DQNs, and the associated code gives me errors. The (first) error message suggested a fix, but in trying to implement it I ran into dependency issues and now I don’t know where I stand. I should be able to run it on the remote platform provided by the company associated with the author, but having bought one book there I have difficulty registering a second or third. I should eventually be able to work on their REPL platform, but it might take a while. The book was only published a week ago. I guess I can get something out of it without running their code. I don’t know Tensorflow well enough to be able to hack problems like this (yet).
On further thought:
One reason for using Tensorflow is the large amount of learning material available which uses that library. However it seems the change from Keras 2 to Keras 3 was sufficiently large to break older code. I’m rethinking my decision. Besides, it’s the principles that I need to learn, not any specific implementation. Of course getting code up and running helps learning principles, so a bit of a chicken and egg situation. I just need to master one library sufficiently that I can implement anything in it. I’m leaning towards PyTorch.