Timeline for Are bandits considered an RL approach?
Current License: CC BY-SA 4.0
10 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Mar 24, 2023 at 13:10 | comment | added | Ynjxsjmh | If there is only one state in the bandit problem, so what is it actually? Is it possible to describe it in detail? Give a cartpole problem, the state could be cart position, cart velocity, pole angle, pole angular velocity. In the bandit problem, my understanding is that the state can be the combination of the pattern, if you get a specific pattern you get the corresponding reward. However, it is hard to model and not only one. So when we say there is only one state, what we are referring to? | |
| May 3, 2020 at 20:15 | vote | accept | user5093249 | ||
| May 3, 2020 at 20:15 | comment | added | user5093249 | Thanks @nbro . In fact, in one of his lectures on bandits, Tor Lattimore referred to bandits as “baby RL”, but I wouldn’t use that term in a formal context :) Szepesvari also includes bandit algorithms in his book Algorithms for Reinforcement Learning (sites.ualberta.ca/~szepesva/rlbook.html , Section 4.2.1). In any case, I think the key difference is as they mention it “the learner's available choices and rewards tomorrow are not affected by their decisions today.” | |
| May 3, 2020 at 16:54 | history | undeleted | nbro | ||
| May 3, 2020 at 0:32 | history | deleted | nbro | via Vote | |
| May 3, 2020 at 0:28 | history | edited | nbro | CC BY-SA 4.0 |
added 76 characters in body
|
| May 3, 2020 at 0:21 | history | edited | nbro | CC BY-SA 4.0 |
deleted 95 characters in body
|
| May 2, 2020 at 23:09 | history | edited | nbro | CC BY-SA 4.0 |
deleted 12 characters in body
|
| May 2, 2020 at 23:03 | history | edited | nbro | CC BY-SA 4.0 |
added 1 character in body
|
| May 2, 2020 at 22:55 | history | answered | nbro | CC BY-SA 4.0 |