Q-Learning Explained - A Reinforcement Learning Technique

brilliant

so nice

You’re the GOAT

Q-Star Lizard Gang 2024

nice explanation deeplizard

I guess crickets make sound, so lizard can take that as input as well to take the path

Very good content, I watched the videos in this playlist to prepera for my exam. Thank you 😊

Are you here after Reuter's article on OpenAI's q*?

Hey, I loved your video. Thank you so much

Your work is simply incredible. Thank you!

she has the most annoying voice ever jesus christ

more B roll video crap without code or mathematics, pandering advanced topics to idiots. this is a waste of human hours.

I'll tell you what I really don't get, it seems the equation only updates the q table based on the current and next state. But the Bellman equation seems to imply that all future states are considered, is there some recursion thing going on?

Really nice video, thanks for the clear explanations!

Thanks so muuuuch !

O' the puns, ... exploit vs explore

Amazing video!

Thank you!

Won't a square become empty when the cricket(s) is(are) eaten?

Link to the talk that appeared at the end of the video?

Why you are so so clear in explaining, I mean why others fail to deliver the tutorials with such clarity like you do?? I dont know whats wrong with everyone. Omg you are impressive.

I am too stupid to understand the video.. My bad..

your videos are awsome! Please correct the corresponding quiz since the answer is uncorrect to me. Could you do a video explaining the first three steps and how the q-tables updates. This would really help to understand how the update works. thank you!

u r an AI, so nicely explained all these hard concepts so easily. thank u so much

How are you so good at explaining😍😍😍😍

Hey @deeplizard,
Many thanks for this video. I'm reading 'Reinforcement Learning: An Introduction, Second Edition by Richard S. Sutton and Andrew G. Bartow' and I'd like to know whether Q- learning technique described here is the same as dynamic programming explained in the book?

Is the exploration vs exploitation part only a part of the training or does it happen also when actually using the learned q table and also can the policy be that "Take the action which has the largest q value and sometimes explore" (eq can that be an example of a policy in this case)? So the policy is just the probability of taking some action in a state so can the policy just be written as: "Take the action which has the largest q value" (this is just an example for exploitation)?

Good balance of exploration and exploitation will bring good results in life too! We are all lizards! :)

Can't wait for coming episodes, because this series is amazing! And they/you helped me a lot. Thank you so much! <3

PHENOMENAL! Your videos are THE BEST! Can you PLEASE PLEASE PLEASE do a series on Actor-Critic methods!!

This was an exciting video, finally, we are getting to the good stuff.

Awesome explanation. I like this

please remove the sound played with the logo at the start of video. the sound is very bad especially when one listens it on head phones.

Really good work

Better than my engineering teacher.

These series are so so informative !! I wish you could make videos on dynamic navigation techniques using DRL

merci merci
hats off

AMAZING SERIES! Absolutely loved it!

Your voice is just amazing 😍😍😍😍😍

channel name is creepy but explanation is amazing...

you saved my life bro

Thanks for the good explanation and all your work. A little hint if I may: Don't explain the words using the same words: Exploitation and Exploration

You say that Q-learning tires to find the best policy. However, I thought q-learning is an off-policy algorithm. I also have troubles understanding the on/off policy concept.

Luv u babe.

exploration of reinforcement learning is going fine !

What should I do in case of continuous tasks? Like in Flappy Bird (if its continuos, but anyway), I guess Q Table would be infinite here or just would have big fixed value to save memory. Can you give some recommendations or explain please? Wanna start implementation, but dont know how Q Table should look like in this case and how to interact with it correctly ( and i hope there will be no other surprises lol )