MIT 6.S191 (2023): Reinforcement Learning

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MIT Introduction to Deep Learning 6.S191: Lecture 5
Deep Reinforcement Learning
Lecturer: Alexander Amini
2023 Edition
For all lectures, slides, and lab materials: introtodeeplearning.com
Lecture Outline:
0:00 - Introduction
3:49 - Classes of learning problems
6:48 - Definitions
12:24 - The Q function
17:06 - Deeper into the Q function
21:32 - Deep Q Networks
29:15 - Atari results and limitations
32:42 - Policy learning algorithms
36:42 - Discrete vs continuous actions
39:48 - Training policy gradients
47:17 - RL in real life
49:55 - VISTA simulator
52:04 - AlphaGo and AlphaZero and MuZero
56:34 - Summary
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Опубликовано:

19 июн 2024

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Комментарии : 73

@BehindTheBackground 4 месяца назад

Excellent slides and explanations!

@mehmetburakguldogan6815 Год назад

Very good work. Seen many lectures on the topic but this is by far the best one and very intuitive. Thank you for sharing.

@nikteshy9131 Год назад

Wow, Thank very much you )) 🥰🥰😊

@muhammadalikhan5003 5 месяцев назад

Amazing lecture delivery. No words to thank you for sharing this wonderful resource for free. Thanks, MIT as well.

@bohaning 4 месяца назад

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@cyrusmobini1321 Год назад

Great as always, thanks for being consistent

@bohaning 4 месяца назад

@nageshwararaov118 Год назад

Thank you very much. 😊

@sirabhop.s Год назад

Thank you so much

@TheEgesko Год назад

Great video! 🙏

@imZoox 7 месяцев назад

haha at 19:50, William Lin the CP legend is answering the question :D Its so weird, I am not even from the US neither I study there but I recognize a student from his voice at MIT in an MIT online lecture :D

@khalidalsaleh3858 Год назад

Thanks!

@blas.duarte Год назад

Great!

@pavalep Год назад

Thanks for explaining complex Deep Learning and Reinforcement principles in a simplistic manner 🙌👍

@bohaning 4 месяца назад

@yuqiwang3296 Год назад

great thanks for the course!❤

@bohaning 4 месяца назад

@user-ff9hy6qz5t 7 месяцев назад

Thank you so much! I loved the lecture, and I'm learning so much! Im only 16 now, but I hope I can one day get into MIT or another great university that teaches this well!

@bohaning 4 месяца назад

@ReeceGao 8 месяцев назад

It is so clear. Thank you very much!

@bohaning 4 месяца назад

@franco-parra 6 месяцев назад

Great lecture. To be precise, at 24:37, you propose the 'target' as a function of the best action a' in some state s', but you don't explicitly define where this s' comes from. I may be mistaken, but I believe that this s' essentially represents the state s in the next step (t+1), as demonstrated in ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-wDVteayWWvU.html (at 14:45). I hope this information is useful to someone.

@esthertschache 6 месяцев назад

Great video!

@hilbertcontainer3034 Год назад

~wow my favorite area about AI =] cant wait to finish the lecture

@bohaning 4 месяца назад

@prithvishah2618 Год назад

Thank you so much :)

@bohaning 4 месяца назад

@xzuanaja2746 Год назад

This is so great! but unfortunately due to my limited English, I didn't understand some parts. Hopefully in the future there will be subtitles in Indonesian or other languages, thank you very much!

@master7738 5 месяцев назад

you can use subtitles if you want

@vahidg1500 9 месяцев назад

Thank You, Ostad Amini, But how can I find some code examples for policy learning like ppo?

@saprogrammer2702 9 месяцев назад

Dude, this guy did such a good job!!!!

@bohaning 4 месяца назад

@jennifergo2024 6 месяцев назад

Thanks for sharing!

@bohaning 4 месяца назад

@seanwalsh358 9 месяцев назад

Great lecture from a great instructor.

@bohaning 4 месяца назад

@sapienspace8814 Год назад

@ 50:00 Very impressive work, VISTA!

@bohaning 4 месяца назад

@MrPejotah Год назад

Once again a great lecture. I have a challenge, and I wonder if you can help me. I'm currently implementing a NN to determine customer satisfaction through a set of inputs that translate behavioural patterns (think # of complaints with our customer service, rate of usage of our services, etc.), and I'd like to know how much each input i'm using contributes to the overall satisfaction score. I imagine this would involve performing the gradient of the output node (a single one in this case), to each input. Is there any lecture where you go into the details of this, both the math and tensorflow code? Thanks in advance!

@bohaning 4 месяца назад

@smftrsddvjiou6443 6 месяцев назад

I recommend Barto Sutton „Reinforcement Learning“, 1st Edition, way,way better than the newer 2nd Edition.

@kritsaphongphuthibpaphaisi1509 Год назад

Great lecture

@bohaning 4 месяца назад

@agenticmark 4 месяца назад

Glad to see ML can figure out what I did as an 8 year old with a stack of quarters :D

@jiunyen5586 Год назад

Thanks for the thorough vid! I'm a bit lost @ 39:31 on where the "-0.8" velocity come from. The closest I'm trying to interpret is given the mean=-1 and var=0.5 the prob of norm dist at mean would be about 0.8... and since your going the negative direction to action a, then it becomes -0.8 ?? But this interpretation seems wrong since the mean should indicate the direction and velocity of action a, while the prob is for computing the loss. So.... what am I missing here? Thanks!

@gnikhil335 Год назад

when you say " the prob of normal distribution at mean would be around 0.8" where did you get 0.8 from ? (the maximum value of this distribution is 0.564 at mean ) and secondly I think he is using 0.8 m/s as an example ( its a random value which you might get after mapping it back to a speed variable in your game )

@jiunyen5586 Год назад

@@gnikhil335 Good call! I misused that variance for std. My mistake. And I also really should've said likelihood there. But yeah, really I was just trying to figure out why he said the mean is centered at -0.8 but also shows a mean of -1 for the predicted params of pdf. As in are they just separate random examples or are we using a pdf with mean=-1, var=0.5 to determine the prob when speed is -0.8, which also doesn't seem likely since I thought we would use the velocity with the max likelihood (i.e. mean).

@MrMonkeyMana Год назад

Can you teach AI to play City Skylines.

@herikaniugu 8 месяцев назад

RL is so good for optimizing the trading strategies

@bohaning 4 месяца назад

@ojasvisingh786 Год назад

👏👏

@Gabcikovo Год назад

54:38

@madhusudhanreddy9157 Год назад

Hi Alex, Could you please suggest any best platform(online coding) that works properly for Reinforcement Learning, In our local systems, are getting errors(system dependencies). Even google colab is showing error when using gym library Thanks Your RU-vid Follower

@xpcalc446 Год назад

Have you try to solve those errors by installing the the correct version of the packages?

@bohaning 4 месяца назад

@user-xd4cl3qd8x 11 месяцев назад

Oh my God, he is so Handsome. And your spoken, lecture delivery, and fluency in RL in as awesome as your looks are....🤩 focusing on the speaker more than the slides. May Allah Almighty bless you man

@bohaning 4 месяца назад

@forheuristiclifeksh7836 Месяц назад

7:00

@DonReichSdeDios 7 месяцев назад

An apple with a byte❤ ✒️ fellow August 13th🤳🏿

@forheuristiclifeksh7836 Месяц назад

14:25

@Achielezz 5 месяцев назад

You say state-action-pear but show an apple, I AM CONFUSION! AMERICA EXPRAIN! :) Loved the lecture, really well done.

@SantoshKumar-hx2ig Год назад

Lecture 7 ?

@AAmini Год назад

Lecture 7 is having some technical difficulties so it will be published tomorrow same time (10am ET) -- sorry for the delay!

@SantoshKumar-hx2ig Год назад

@@AAmini I am very happy for reply within few minutes. Today I feel the power of mit .

@AAmini Год назад

Thank you for your understanding :)

@smftrsddvjiou6443 6 месяцев назад

Now, he knows that Q values can be converted into Probability?

@roadto300kusdbtc7 Год назад

once again, audio is super quiet. Had to turn the volume to 100. Fire the audio guy lol

@davidkamran9092 10 месяцев назад

SEALCLATCONTITOIN - YALL NEED TO INCORPORATE HARD-CODED TRAJETORIES LIKE POLITICAL VIEWS IN DEEP LEARNING .. THE SYSTEM DYNAMICS CHANGE BASED ON POLITICAL MODALITIES