Reinforcement Learning from scratch
Вставка
- Опубліковано 21 тра 2024
- How does Reinforcement Learning work? A short cartoon that intuitively explains this amazing machine learning approach, and how it was used in AlphaGo and ChatGPT.
Part 1 of 3.
0:00 - intro
0:13 - pong
0:28 - the policy
0:51 - policy as neural network
1:32 - supervised learning
2:51 - reinforcement learning using policy gradient
4:24 - minimizing error using gradient descent
4:45 - probabilistic policy
5:01 - pong from pixels
6:58 - visualizing learned weights
8:18 - pointer to Karpathy "pong from pixels" blogpost
this is video is super underrated. In fact the whole channel is underrated.
Your Channel IS SO GREAT, I share with all my eng friends for you to get more visibility!
This is really awsome! It's the best video that explains DRL in such an easy to understand way!
This video is amazing. You explained everything in such a simple manner. I am feeling really motivated to learn more about reinforcement learning and neural networks after watching this.
agi: 1. ai develops understanding of win-loss conditions and sets policy params (inputs & actions) accordingly. 2. ai creates (= designs & builds) training env(s). 3. ai iterates, avals & adjusts policy parameters accordingly 4. done (or validation run(s) w/ human(s))
I don't know how I stumbled upon this video but that was very interesting and intuitive to understand. Thank you.
Your videos are great. Looking forward to more!
Great video, very helpful, easy to understand.
Amazing video as always :)!
Excellent. Congratulations ❤
Super helpful! Thank you 🙏🏽
I really like the way you visualize what you are talking about. Thank you for putting in the effort!
This was so surprisingly great :3
Excellent content!
Very very underrated channel
Underrated, two Rs
@@benc7910 thank ya sir
Excellent
thank you for this!
Thank you!!!
Can you playlist each one of your topics plz?
I wanted to post on Twitter(X) your video topics but could only post a single video at a time.
Great content by the way. Ty very much.
Your perspective on some topics helped me a lot to get a more intuitive understanding.
Good idea! Here's one on generative AI:
ua-cam.com/play/PLWfDJ5nla8UoR8P7AGqVw7ZPjXajUFLMo.html
Here's one on reinforcement learning
ua-cam.com/play/PLWfDJ5nla8UoexEaLqVMw7q3Ft0vRYscL.html
Here's one on LLMs + text-to-image
ua-cam.com/play/PLWfDJ5nla8UoG2mvvHs_OS0asAKC5HJeu.html
@@g5min Ty!
What is your reward function for the pong game? I did a similar pong game and I couldn't get it to learn.
That was dope
Brilliant
how many layers should such network have
but by what number do you change the weights like you never told us
Simple Reinforcement learning is extremely dangerous in certain nonstationary environments 😅
whats the name of this video game ?
that was good
Can you share the source code for this project
You can follow the link to the Karpathy site at the end of the video, repeated here:
karpathy.github.io/2016/05/31/rl/
Imagine using reinforcement learning in quantitative finance 😊
ah yes, reinforcement learning. a fundamental computer graphics technology
I think that character/game-AI is pretty central to graphics
Why so negative?
@@g5minespecially AI image generation or processing nowadays