Timeline for What is the "Hello World" problem of Reinforcement Learning?
Current License: CC BY-SA 4.0
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| Sep 15, 2020 at 6:43 | comment | added | mugoh | Definitely, if this answer presents Q-learning as a "useless toy algorithm", that's very misleading. I've noted how the use of program and problem (as you explain it) contributes to this. I've attempted to edit the answer to alleviate that, as much as I could. Thanks @nbro! My intention was not to mislead. | |
| Sep 15, 2020 at 6:32 | history | edited | mugoh | CC BY-SA 4.0 |
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| Sep 14, 2020 at 22:09 | comment | added | nbro | Although you are talking about tabular Q-learning, I think that saying that Q-learning is the "Hello World" program (not problem, as in my answer) of RL is misleading, because it makes it seem like Q-learning is just a "toy useless algorithm", but, of course, it's not. Q-learning with function approximation (specifically, neural networks) was used to solve/play the Atari games with superhuman performance in many cases. So, Q-learning, although quite simple, can be used to solve non-trivial problems, so it's a powerful algorithm, and that's why your suggestion is misleading. | |
| Sep 14, 2020 at 13:40 | comment | added | Mast |
So, the ML version of while(!worldOnFire){flames++}?
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| Sep 14, 2020 at 9:43 | history | edited | mugoh | CC BY-SA 4.0 |
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| Sep 14, 2020 at 9:33 | history | edited | mugoh | CC BY-SA 4.0 |
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| Sep 14, 2020 at 9:24 | history | edited | mugoh | CC BY-SA 4.0 |
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| Sep 14, 2020 at 9:01 | history | edited | mugoh | CC BY-SA 4.0 |
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| Sep 14, 2020 at 8:56 | history | answered | mugoh | CC BY-SA 4.0 |