Lessons Learned Reproducing a Deep Reinforcement Learning Paper
<i>"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."</i><p>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?<p>Having a "single shot", with long iteration cycles, indeed does something strange to the way you approach programming and bugs and program design.<p>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"…<p><i>On topic</i>: I wrote a rant about the "Mummy Effect" of trying to reproduce ML papers (which we do regularly): <a href="https://rare-technologies.com/mummy-effect-bridging-gap-between-academia-industry/" rel="nofollow">https://rare-technologies.com/mummy-effect-bridging-gap-betw...</a>