Reinforcement Learning from scratch
ฝัง
- เผยแพร่เมื่อ 6 มิ.ย. 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 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.
I don't know how I stumbled upon this video but that was very interesting and intuitive to understand. Thank you.
This is really awsome! It's the best video that explains DRL in such an easy to understand way!
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))
Your videos are great. Looking forward to more!
Great video, very helpful, easy to understand.
Amazing video as always :)!
This was so surprisingly great :3
Excellent. Congratulations ❤
I agree once you see how it all works it seems like 1s and zeros give me some feed back on r/grand unified theory or cosmo knowledge
Excellent content!
I really like the way you visualize what you are talking about. Thank you for putting in the effort!
Very very underrated channel
Underrated, two Rs
@@benc7910 thank ya sir
Super helpful! Thank you 🙏🏽
thank you for this!
What is your reward function for the pong game? I did a similar pong game and I couldn't get it to learn.
Excellent
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:
th-cam.com/play/PLWfDJ5nla8UoR8P7AGqVw7ZPjXajUFLMo.html
Here's one on reinforcement learning
th-cam.com/play/PLWfDJ5nla8UoexEaLqVMw7q3Ft0vRYscL.html
Here's one on LLMs + text-to-image
th-cam.com/play/PLWfDJ5nla8UoG2mvvHs_OS0asAKC5HJeu.html
@@g5min Ty!
Thank you!!!
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
that was good
whats the name of this video game ?
Simple Reinforcement learning is extremely dangerous in certain nonstationary environments 😅
Imagine using reinforcement learning in quantitative finance 😊
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/
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