oneraynyday

20 ms·

1.
by oneraynyday 6y ago
Noted, will abbreviate title :)
2.
Interviewing During Covid – Google/Apple/ByteDance/Databricks/Citadel/HRT/JS(oneraynyday.github.io)
3 points by oneraynyday 6y ago|3 comments
3.
Reinforcement Learning – Comprehensive Introduction to Monte Carlo Methods(oneraynyday.github.io)
5 points by oneraynyday 8y ago|0 comments
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Reinforcement Learning – Introduction to Markov Decision Processes (MDPs)(oneraynyday.github.io)
3 points by oneraynyday 8y ago|0 comments
5.
by oneraynyday 8y ago
Ah yes, I didn't want the reader to fuss too much about the idea of a preference, since it's really just an analog of un-normalized pi(modulo the exponentiation part). It doesn't have a strict definition nor is it a formal te
6.
by oneraynyday 8y ago
Hey Joshua, thanks so much for the criticism. I do see your point towards epsilon greedy vs. regret minimization. I will add a section about that before presenting UCB1. I also will add more information in the epsilon-greedy strategy sectio
7.
by oneraynyday 8y ago
Thanks for the reply! My audience is for someone who is early into their undergrad degree, doing math or computer science. Which parts do you think is not communicated clearly? I can make an edit later today :)
8.
by oneraynyday 8y ago
Great suggestion! The blog was based on a large portion of the book. A friend of mine asked for a version of the first chapter that was digestible for an audience that is in high-school to undergrad college level. I wrote this blog with tha
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by oneraynyday 8y ago
Yes, it is as you stated. Due to the fact that bandits are stateless, there is no state parameter in $q_ (a,s)$. From where I learned it, this could arguably be an abuse of notation to use $q_ $ in the same context. In my newer entry(which
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Reinforcement Learning – Bandit Problems(oneraynyday.github.io)
218 points by oneraynyday 8y ago|40 comments