Foundation of Q-learning | Temporal Difference Learning explained!
ฝัง
- เผยแพร่เมื่อ 8 ก.พ. 2025
- Let's talk about the foundation concept of Q-learning, SARSA called Temporal Difference Learning.
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You have a gift to teach and I'm very thankful to find someone who breaks down concepts so simply and easy
to digest
Thanks so much for the kind words. I really appreciate this
Totally agree!
The breakdown of the 1 sentence explanation is so useful
Nice, quick and straight to the point.
man, your explanation is gorgeous ! you are remarkable in explaining complex things. Keep doing what you are doing :) I wish you much luck with your channel
You are a legend dude. Was stuck in an assignment and this video helped me!
Fantastic video...I have a RL assignment due and I had no idea wht TD error even meant. You are great at explaining
i wasnt expecting such a good explanation.
I'll need to check out more of your videos... That is so well explained!!
Great work ❤
thank you so much! this is an awesome resource!
Excellent
can you prepare a video for Double Q-Learning Network
and Dueling Double Q-Learning Network
please
I'm craving for some Tempuras now
This is best . Thanks!
awesome explanation!
Thanks a lot. This is real helpful. I will check out the bellman equation video as well
Great video !!!!
Great lecture 😉
this video not ust explain q value, but also value function, action value function, episode, etc
great video, thanks!
You are very welcome. Thanks for commenting
why use an episodic problem as an example for 1-step TD ? the advantage of TD is for non-episodic problems.
TD uses previous value to bootstrap the current estimate, in this case shouldn't the table be initialized to R for each S,
instead of zeroes ?
Post more about GANs
While watching the video, it occurred to me how do ants find paths and remember the path, change it when disturbed.
💖💖
why haven't you given true credits to the original video creator. The voice is not yours we know it. You are simply lip-syncing the audio. You could've used the video and used your own true voice! I feel this is not your voice! 99.99% !