I have a basic Policy Gradient algorithm up and running at last. The code comes from a Udemy course by Phil Tabor (specifically on AC methods), which I modified very slightly to use with my algorithm testing app. It’s not performing all that well, and I don’t fully understand the code, but it’s a starting point. From here I can study the ‘inner workings’ more closely with the help of the debugger to step through code and see what it actually does. Also my reading will have a solid point of reference. I find it hard to think about these things in purely abstract terms. And I can work on modifications and improvements.
I think I’ve reached the most important milestone, moving from ‘can I do this?’ to ‘this is doable’. I’ve certainly had huge doubts over the past couple of months, or longer. This field is more ‘math heavy’ than any I’ve previously tackled, and I find that very challenging. There’s the question how much do I need to understand the maths that underpins the algorithms. I’ve always found it easier to do something if I understand what I’m doing, rather than blindly following instructions, but many people don’t seem to labour under that constraint. Anyway, I now feel confident to move forward.