3 ms·
The mind blowing thing is that it actually works, and with rand(0,1), it'll get you results with only 10 guesses (though it's a bit cheating haha). Fun fact: a
by mikeshi42 8y ago
The mind blowing thing is that it actually works, and with rand(0,1), it'll get you results with only 10 guesses (though it's a bit cheating haha).
Fun fact: a policy of [1,2,3,4] will solve the game.
When I started off the article, I was thinking of doing some kind of evolutionary-based policy search but turns out none of that is even necessary.
I hope to have a more technical follow up w/ deeper analysis and hopefully another one that goes into harder games where random guessing is okay, but there are more "sample efficient" methods.
- X6S1x6Okd1st 8y agoYeah I've been meaning to work my way up the complexity ladder on openai's gym. Seeing that it can be reasonably done by a random walk and the fact that it's only 4 dimensions kind of makes me want to do a uniform convering of the space and visualize the results.
- mikeshi42 8y agoYou should! I did some analysis myself a bit, have to dust it off to make it bit more presentable. There are definitely clear boundaries of "good" policies.
- wholemoley 8y agoIf you do decide to do an evolutionary-based policy, I highly recommend messing around with NEAT (https://github.com/CodeReclaimers/neat-python https://github.com/CodeReclaimers/neat-python).. I have successfully used it to play a number of SNES/Genesis games. I even made a tutorial series. https://www.youtube.com/watch?v=pClGmU1JEsM&list=PLTWFMbPFsvz3CeozHfeuJIXWAJMkPtAdS https://www.youtube.com/watch?v=pClGmU1JEsM&list=PLTWFMbPFsv... Apologies for the shameless plug.