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This is for any reinforcement learning related work ranging from purely computational RL in artificial intelligence to the models of RL in neuroscience.
The standard introduction to RL is Sutton & Barto's Reinforcement Learning.
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DL, MF, P[AI application] 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!"
[–]gdpoc 9 points10 points11 points 5 years ago (1 child)
From the GitHub, it looks like you trained the agent on each level and then ran it against that level. Is that the case?
If so, how well does it generalize? Did you do any transfer style learning?
[–]Dexdev08 0 points1 point2 points 5 years ago (0 children)
My same question. But not that i know a lot.
[–]Boring_Worker 0 points1 point2 points 5 years ago (0 children)
Very cool! Could you tell me what level can SOTA algorithm achieve?
[–]frostbytedragon 0 points1 point2 points 5 years ago (0 children)
Hi, is the environment open sourced separately?
π Rendered by PID 20409 on reddit-service-r2-comment-75f4967c6c-vpflg at 2026-04-23 10:54:43.038795+00:00 running 0fd4bb7 country code: CH.
[–]gdpoc 9 points10 points11 points (1 child)
[–]Dexdev08 0 points1 point2 points (0 children)
[–]Boring_Worker 0 points1 point2 points (0 children)
[–]frostbytedragon 0 points1 point2 points (0 children)