= 27d567bc2d48d9f40e9ccc20ea370b15)
ShardPhoenix comments on [LINK] Deep Learning Machine Teaches Itself Chess in 72 Hours - 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 (15)
Although it's not better than existing solutions, it's a cool example of how good results can be achieved in a relatively automatic way - by contrast, the evaluation functions of the best chess engines have been carefully engineered and fine-tuned over many years, at least sometimes with assistance from people who are themselves master-level chess players. On the other hand this neural network approach took a relatively short time and could have been applied by someone with little chess skill.
edit: Reading the actual paper, it does sound like a certain amount of iteration, and expertise on the author's part, was still required.
edit2: BTW, the paper is very clear and well written. I'd recommend giving it a read if you're interested in the subject matter.
Thank you for recommending to read the paper, I don't think I would have otherwise and I greatly enjoyed reading it!
But how much of its performance comes from the neural network learning some non-trivial evaluation function and how much comes from brute-forcing the game tree on a modern computer?
If the neural network was replaced by a trivial heuristic, say, material balance, how would the engine perform?
In the paper they start with just material balance - then via the learning process, their score on the evaluation test goes from "worse than all hand-written chess engines" to "better than all except the very best one" (and the best one, while more hand-crafted, also uses some ML/statistical tuning of numeric params, and has had a lot more effort put into it).
The reason why the NN solution currently doesn't do as well in real games is because it's slower to evaluate and therefore can't brute-force as far.