Principle result is that by studying a sequence of small problems in ML, I could predict the outcome of experiments on orders-of-magnitude larger problems 🤯
I worked on Hex. Hex is a board game, with all the strategic depth of Go but also a much simpler rule set. Crucially, Hex on small boards is easy, and Hex on big boards is hard!
Dec 20, 2019 • 4 tweets • 2 min read
I can't recall any _techniques_ that knocked me off my chair, but there have been a couple of papers on training phenomena which have had a serious impact on how I think about RL:
'Meta learner's dynamics are unlike learners': you ask a regular NN to learn a transformation and it'll learn the component with the largest eigenvalue first, then the second largest, etc etc. A meta-learner will learn all the components simultaneously! arxiv.org/abs/1905.01320
