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.
What a great series! I have a question for the experts... was it necessary to map velocity as an input? I'm guessing it's not absolutely necessary and was done to make the training faster? My guess is based on the assumption that the timing of the ball x/y changes to the inputs have an effect, but I may be wrong.
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
I get how the model can see moves and output up or down action. But I don't get how model tracks the score for rewards etc Can someone explain how the reward is fed into model
@ not quite, let’s say A=5 and B=10 are outputs from 2 nodes after ReLU. sometimes these outputs are multiplied by weights going to the next layer, maybe not in his example not sure. but, all the outputs after relu are summed together THEN squished with this sigmoid function. formula: S(x)= 1/(1+e^-x) where e is euler’s number (a constant) and x is the sum of the outputs of the previous nodes (so in your example x=5+10=15) this would give the output: S(15)=.999… (very close to 1 in this example) if you look up the graph you’ll see it quite literally looks like that squiggle worm he showed. but yeah sigmoid pretty much just normalizes (squishes) the sum of the outputs to fit between 0 and 1
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this is video is super underrated. In fact the whole channel is underrated.
Maybe i should follow the channel then 😅.
This was my first vid, and the explanation was really well simplified
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.
Too beautiful you can watch this kind of videos all the day without get bored
This was so much easier to understand than the other RL videos that came up when I searched this topic
Can we have the code for this
Lol😅😅😅😅😅😅
Very very underrated channel
Underrated, two Rs
@@benc7910 thank ya sir
This is really awsome! It's the best video that explains DRL in such an easy to understand way!
I really like the way you visualize what you are talking about. Thank you for putting in the effort!
This is super underrated video
Your videos are great. Looking forward to more!
This was so surprisingly great :3
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))
Great video, very helpful, easy to understand.
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. Congratulations ❤
Excellent content!
Amazing video as always :)!
thx and god bless u, regards from hong kong, china, merry chirstmas... ^__^
Thanks a lot for this one! 😊
What a great series! I have a question for the experts... was it necessary to map velocity as an input? I'm guessing it's not absolutely necessary and was done to make the training faster? My guess is based on the assumption that the timing of the ball x/y changes to the inputs have an effect, but I may be wrong.
Can i speed up training if i know exact picels thst have high importance and are static?
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!
Excellent
Superb
Super helpful! Thank you 🙏🏽
Thank you!
What is your reward function for the pong game? I did a similar pong game and I couldn't get it to learn.
I get how the model can see moves and output up or down action. But I don't get how model tracks the score for rewards etc
Can someone explain how the reward is fed into model
how many layers should such network have
i just have a quastion, what is that thing ? 6:20 its like a worm ?
like. i didnt take it in my math class.... im 16 years btw
i mean the one u added
Sigmoid function. Basically squishes the results from what the ReLU spits out to a number between 0 and 1
@@insecureprince120 like this?:
A = 5
B = 10
[this thing] 0.5 = 7.5 ?
if yes its the same "lerp node" in unreal engine
@ not quite, let’s say A=5 and B=10 are outputs from 2 nodes after ReLU. sometimes these outputs are multiplied by weights going to the next layer, maybe not in his example not sure. but, all the outputs after relu are summed together THEN squished with this sigmoid function. formula: S(x)= 1/(1+e^-x)
where e is euler’s number (a constant) and x is the sum of the outputs of the previous nodes (so in your example x=5+10=15) this would give the output: S(15)=.999… (very close to 1 in this example)
if you look up the graph you’ll see it quite literally looks like that squiggle worm he showed. but yeah sigmoid pretty much just normalizes (squishes) the sum of the outputs to fit between 0 and 1
@@insecureprince120 bro i waste your time, idk what im gonna say, thank you.. ig?, god bless you
thank you for this!
but by what number do you change the weights like you never told us
Facing the same problem
Thank you!!!
Brilliant
Simple Reinforcement learning is extremely dangerous in certain nonstationary environments 😅
whats the name of this video game ?
that was good
Pls o want the code plsss
ich bin confuzzled (i dont actually speak german FYI)
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
he just HAD to mention his macbook air...