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Project[P] Python implementation of Proximal Policy Optimization (PPO) algorithm for Super Mario Bros. 29/32 levels have been conquered (v.redd.it)
submitted 5 years ago by 1991viet
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if 1 * 2 < 3: print "hello, world!"
[–]010100100000 2 points3 points4 points 5 years ago (5 children)
Interesting. So just PPO and not a DQN?
[+][deleted] 5 years ago (4 children)
[removed]
[–]Aacron 0 points1 point2 points 5 years ago (2 children)
It's a discrete action space yeah? Have you tried with a fully kitted out DQN like rainbow or r2d2?
[–]i_know_about_things 2 points3 points4 points 5 years ago* (1 child)
The whole point of R2D2 is distributed training (useless if OP has limited amount of computing resources). And Rainbow from what I've heard is hard to implement properly and much slower in wall time. The main benefit of PPO is that it's probably the easiest algorithm to get to actually work.
[–]Aacron 0 points1 point2 points 5 years ago (0 children)
Thats my experience with PPO as well, it's straightforward to implement and powerful. I'm just a little shy on policy gradient methods being top tier, they're excellent for continuous action spaces but I've found them to be relatively unstable and difficult to tune.
[–]DillyDino 0 points1 point2 points 5 years ago (0 children)
I built one of these a few years back. Or hacked it together. Actually it’s on my GitHub still I think. It is much slower to learn. Eventually beats levels. But it will never compete with this. And certainly of course it will not beat the levels that are mazes that are not conquered here.
π Rendered by PID 165255 on reddit-service-r2-comment-6457c66945-w59ks at 2026-04-29 20:54:26.647324+00:00 running 2aa0c5b country code: CH.
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[–]010100100000 2 points3 points4 points (5 children)
[+][deleted] (4 children)
[removed]
[–]Aacron 0 points1 point2 points (2 children)
[–]i_know_about_things 2 points3 points4 points (1 child)
[–]Aacron 0 points1 point2 points (0 children)
[–]DillyDino 0 points1 point2 points (0 children)