Reinforcement Learning: on-policy vs off-policy algorithms

Let's talk about on-policy vs off-policy algorithms in reinforcement learning
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[@MrFalk358]

Ok i will indulge your quiz time questions since your videos are really great!
Question 1: A is correct. it would not learn at all, since the target policy is the policy which we are trying to learn. Setting it fixed would imply it not changing, which would imply it staying random, therefore we are not learning
Question 2: Im not completely sure but i would say B is correct, since SARSA uses its target policy both to choose action and to "look" (by taking the action according to the target policy) at its follow up state
Hope more people comment so the algorithm boosts your channel!

[@CodeEmporium]

Ding ding ding! You have been paying attention :) Also thanks a ton for indulging me here. I am trying new ways to make sure this content is engaging and educational at the same time. So the more people like yourself that participate, the more I see the value in this content.

[@MrFalk358]

@@CodeEmporium i taking a course on rl at the moment which is quite disorganized, your content definitely helps a ton with understanding!

[@0xabaki]

@@CodeEmporium I love quiz time! It felt best when professors would quiz us on topics so I can re-engage.

[@aitorgonzalezgonzalez9395]

I think i found an error in the summary, you wrote twice "Off Policy RL Algorithms". Apart from that, thanks so much for the video, it helped me a lot.

[@mumbo2526]

Amazing Video, thank you!

[@moaaathkhalil]

Well explained!

[@alonsovalderramahickmann940]

Very nice video man

[@kiranbade9481]

well explained brother

[@zhezhe3351]

Good video！there is a small typo at the summary page about on-policy

[@hugeturnip3520]

Thank you so much dude

[@broccoli322]

Thanks for the video! ☺

[@CodeEmporium]

You are very welcome :)

[@user-xv9qk3iz7b]