Lessons Learned Reproducing a Deep Reinforcement Learning Paper
"Switching from experimenting a lot and thinking a little to experimenting a little and thinking a lot was a key turnaround in productivity. When debugging with long iteration times, you really need to pour time into the hypothesis-forming step - thinking about what all the possibilities are, how likely they seem on their own, and how likely they seem in light of everything you’ve seen so far."Not to glorify by-gone days, but isn't there a certain charm to having to wait your turn to plug punch cards into a mainframe?Having a "single shot", with long iteration cycles, indeed does something strange to the way you approach programming and bugs and program design.Anecdote: my long iteration cycles were caused by my mom restricting access to my C64. I filled page after page (paper!) with code, anxious for any chance to try it. I like to think I learned something during these "mental dry runs"…On topic: I wrote a rant about the "Mummy Effect" of trying to reproduce ML papers (which we do regularly): https://rare-technologies.com/mummy-effect-bridging-gap-betw...