Thanks For putting out the best reinforcement learning tutorial Video I've ever seen. Line by line Brilliant!!!!!!
@kedarzc
23 күн бұрын
Please keep making videos! This is an awesome place to start for beginners..
@dariustrabalza6629
7 ай бұрын
Thanks for taking the time to make these videos. It's been really hard to find up to date information on how to use this stuff. there's not many videos out there. You break things down very simple and i appreciate it greatly. thanks for the hard work!
@Lukas-rj9nr
5 ай бұрын
what a great video. You packed so much in just 12min. Hope you continue to make more videos about RL
@HarshVardhan-m7e
8 ай бұрын
Thanks for this tutorial !
@carsonlind2361
11 ай бұрын
Subscribed and liked. This has been really helpful in getting started. Thank you!
@helims9384
11 ай бұрын
Thanks for the great content. I found something interesting about the algorithm. The performance of the algorithm is highly dependent on the results of the epsilon-greedy exploration. If we don't update the informative Q(s,a) table within some episodes (ex.5,000), the results are terrible. It's interesting that the results are inconsistent.
@panagiotisseventekidis7153
Ай бұрын
great work
@caigao1571
10 ай бұрын
thanks for your explanation
@yahayaibrahimu8954
11 күн бұрын
Good Tutorial. Is there any tutorial discussing about Radio Frequency Reinforcement Learning Gymnasium
@johnnycode
10 күн бұрын
Sorry, I don’t know about that topic.
@kimiochang
5 ай бұрын
Thanks!
@johnnycode
5 ай бұрын
Thank you very much!!!
@vickyli-hk6ir
10 ай бұрын
very clearly!!!
@anissahafid9465
4 ай бұрын
merci d'avoir envoyé le code source de cet vidéo , et merci bien pour ces excellent explication
@AichaHAFID-x2s
4 ай бұрын
Bonjour, je félicite pour ces excellent vidéo, je suis entraîné de programmer le même algorithme mais avec plusieurs agents , c.a.d on a plusieurs agents et plusieurs obstacles et plusieurs gouls au même temps, et j'ai trouve pas une méthode de modifier ce programme et intégrer plusieurs agents au même, merci d'avoir clarifier le programme qui fait cette opération, , et merci bien pour votre aide
@johnnycode
4 ай бұрын
Thank you, good luck on your work.
@abdelilahbrf
9 ай бұрын
THANKS FOR VIDEO in visual studio code i don't have button for stop and pause how can I activate them or install any extension
@johnnycode
9 ай бұрын
I think VSCode automatically installs the Python extensions when you open a Python file. If that didn’t happen, check out this reference code.visualstudio.com/docs/editor/debugging
@abdelilahbrf
9 ай бұрын
@@johnnycode Thank you so much 👍👍👍👍✔✔✔✔
@Pendemon
9 ай бұрын
Awesome video
@ElisaFerrari-q5i
2 ай бұрын
Based on what do we assign these values to hyperparameters?
@johnnycode
2 ай бұрын
Based on trial and error, or a process called hyperparameter tuning.
@ApexArtistX
11 ай бұрын
awesome tutorial more please...
@johnnycode
9 ай бұрын
Hi, in case you're looking for a Deep Q-Learning video, I've recently released a detailed one: kzitem.info/news/bejne/pouovXuKfHmrn3Y
@muhammadtayyabbaig1071
2 ай бұрын
At the start is the q table accurate?How is the q table made accurate and when does it start to follow it?
@johnnycode
2 ай бұрын
The q-table is not accurate at the beginning. It becomes more accurate by updating with the q-learning formula. In the video, I did not talk about the theory and mathematics behind the q-learning formula.
@Lukas-rj9nr
5 ай бұрын
For a project in uni, I want to train an agend that can behave well on different state spaces. Imagine one agend should be able to solve the FrozenLake-Problem in 5x5, but also in 6x6, 7x7 etc. and also 5x6, 5x7, 6x5, etc. How to do that? Do you have an idea or keywords to search for?
@johnnycode
5 ай бұрын
My video on how to “Build a Custom Gymnasium Reinforcement Learning Environment” kzitem.info/news/bejne/oqV9uJ6GrV-noKg does very similar to what you described. However, you don’t have to create a custom environment, you just have to train the agent on all the different FrozenLake map sizes.
@thefall0190
9 ай бұрын
Will you do this with deep Q-learning version ?
@johnnycode
9 ай бұрын
Yes, I’m working on it. Will share in a few days.
@thefall0190
9 ай бұрын
Thank you !@@johnnycode
@johnnycode
9 ай бұрын
Hi, my Deep Q-Learning video is out kzitem.info/news/bejne/pouovXuKfHmrn3Y Please check it out.
@AichaHAFID-x2s
4 ай бұрын
Je vous remercier infiniment , j'ai déjà voir ce vidéo mais j'ai trouvé pas une méthode pour crier plusieurs robots en même , sachat que leur travail est semblable comme le premier agent, tout on évitons les obstacles et fair recherche de la but ( goal), s'il y a une méthode simple merci d'avoir m'informer et le code source surtout , merci et merci pour vous effort de repondre
@johnnycode
4 ай бұрын
I will try to do some multiagent videos.
@anissahafid9465
4 ай бұрын
@@johnnycode merci et merci, puisque m'intéresse au multi agent ( ou bien multi robots ) et plusieurs goal ( buts ) , si l'un des ces agents trouve un goal il le marque comme fait , et lorsque un autre agent trouve le meme goal il le ignore et complet leur travail de recherche , je vous attend, bon implémentation , bon chanse.
@anissahafid9465
4 ай бұрын
Bonjour , s'il ya des nouveau pour la programmation des multi agent au meme temps , merci infiniment
@rayog2707
3 ай бұрын
what to do to see the Q-table?
@johnnycode
3 ай бұрын
The Q-table is a regular Python array, so you can just use a loop to print the value. In my other video, you can visually see the values on the map: kzitem.info/news/bejne/ko2VsoN4ZmJ6eI4
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