But those video games have a very simple optimal policy. Consider Super Mario: if you see an enemy, step on it; if you see a whole, jump over it; if you see a pipe sticking up, also jump over it; etc.
On Sat, Feb 25, 2017 at 12:36 AM, Darren Cook <dar...@dcook.org> wrote: > > ...if it is hard to have "the good starting point" such as a trained > > policy from human expert game records, what is a way to devise one. > > My first thought was to look at the DeepMind research on learning to > play video games (which I think either pre-dates the AlphaGo research, > or was done in parallel with it): https://deepmind.com/research/dqn/ > > It just learns from trial and error, no expert game records: > > http://www.theverge.com/2016/6/9/11893002/google-ai- > deepmind-atari-montezumas-revenge > > Darren > > > > -- > Darren Cook, Software Researcher/Developer > My New Book: Practical Machine Learning with H2O: > http://shop.oreilly.com/product/0636920053170.do > _______________________________________________ > Computer-go mailing list > Computer-go@computer-go.org > http://computer-go.org/mailman/listinfo/computer-go
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