= 27d567bc2d48d9f40e9ccc20ea370b15)
abramdemski comments on Algorithms as Case Studies in Rationality - Less Wrong
You are viewing a comment permalink. View the original post to see all comments and the full post content.
= 783df68a0f980790206b9ea87794c5b6)
Loading…= b715d02e85f7b3b4ae65ca3d3e450868)
Subscribe to RSS Feed
Powered by Reddit
= f037147d6e6c911a85753b9abdedda8d)
You are viewing a comment permalink. View the original post to see all comments and the full post content.
Comments (39)
Algorithms I find useful that I didn't put in the article:
--Find decent solutions to packing problems by packing the largest items first, then going down in order of size
--Minimum-description-length ideas (no surprise to rationalists! Just occam's razor)
--Binary search (just for finding a page in a book, but still, learning the algorithm actually improved my speed at that task :p)
--Exploration vs exploitation trade-off in reinforcement learning (I can't say I'm systematic about it, but learning the concept made me aware that it is sometimes rational to take actions which seem suboptimal from what I know, just to see what happens)
My classical example for algorithms applicable to real life: Merge sort for sorting stacks of paper.
Only that "Exploration vs exploitation trade-off" is not an algorithm. Reinforcement learning (RL) is pretty much "non-algorithmic" (as Pei Wang would say). ETA: there are specific algorithms in RL (and in -- related -- planning and game playing), but the "trade-off" is a concept; it sure needs to be expressed algorithmically but is it fair to give credit to "algorithmicality" in this case?
Right; when I say "I'm not systematic about it" I mean that I don't purposefully follow a specific algorithm. I would probably benefit from being a bit more systematic, but for the moment, I'm merely trying to "train my intuition".
I would hope that all these algorithms would be applied "non-algorithmically" in Pei Wang's sense-- that is, the ideas from the algorithm should interact dynamically with the rest of my thought process.
I'm rather certain I could implement reinforcement learning as an algorithm. In fact, I'm rather certain I have done so already. If I can point to an algorithm and say "look, that's a damn reinforcement learning algorithm" then I'm not sure how meaningful it can be to call it "non-algorithmic".
I concede, RL is a prototype example of algorithmic learning problem. The exploration vs exploitation trade-off is something that needs to be addressed by RL algorithms. It is fair then to say that we gain insight into the "trade-off" by recognizing how the algorithms "solve" it.
It is also fair to say there is an abstract concept of 'trade off' that is not itself algorithmic.