RL Course by David Silver - Lecture 1: Introduction to Reinforcement Learning
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- Опубликовано: 25 авг 2024
- #Reinforcement Learning Course by David Silver# Lecture 1: Introduction to Reinforcement Learning
#Slides and more info about the course: goo.gl/vUiyjq
Just finished lecture 10 and I've come back to write a review for anyone starting. *Excellent course*. Well paced, enough examples to provide a good intuition, and taught by someone who's leading the field in applying RL to games. Thank you David and Karolina for sharing these online.
I've finished both of them, and I'd say that this one has a better and much more solid content, although the one from udacity is much more light and easy to follow, so it really depends on what you want :)
This course is more thorough and for someone who is looking to make a career in Machine Learning, you should put in the work and do this course.
Thanks for the review!
One of the best , if not the best , courses I've watched!
thanks for the review
0:01 Outline
Admin 1:10
About Reinforcement Learning 6:13
The Reinforcement Learning problem 22:00
Inside an RL angent 57:00
Problems within Reinforcement Learning
Good job. Very thankful :)
thanks
You made the world much easier! Thanks!
Problems within Reinforcement Learning 1:15:53
Thank you for this.
what a wonderful time to be alive!!
thank god we have the opportunity to study a full module from one of the best unis in the world. taught by one of the leaders of its field
The complete set of 10 lectures is brilliant. David's an excellent teacher. Highly recommended!
I love that David is one of the foremost minds in Reinforcement Learning, but he can explain it in ways that even a novice can understand.
hlo, can u please suggeest roadmap for rl.
how lucky we are to have access to this kind of knowledge only with a button ! Thank you all in DeepMind public this course
I was going to say the same. Technology has really made our life easier and better in a lot of ways. But a lot of times we take it for granted.
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1:10 Admin
6:13 About Reinforcement Learning
6:22 Sits in the intersection of many fields of science: solving decision making problem in these fields.
9:10 Branches of machine learning.
9:37 Characteristics of RL: no correct answer, delayed feedback, sequence matters, agent influences environment.
12:30 Example of RL
21:57 The Reinforcement Learning Problem
22:57 Reward
27:53 Sequential Decision Making. Action
29:36 Agent & Environment. Observation
33:52 History & State: stream of actions, observations & rewards.
37:13 Environment state
40:35 Agent State
42:00 Information State (Markov State). Contains all useful information from history.
51:13 Fully observable environment
52:26 Partially observable environment
57:04 Inside an RL Agent
58:42 Policy
59:51 Value Function: prediction of the expected future reward.
1:06:29 Model: transition model, reward model.
1:08:02 Maze example to explain these 3 key components.
1:10:53 Taxonomy of RL agents based on these 3 key components:
policy-based, value-based, actor critic (which combines both policy & values function), model-free, model-based
1:15:52 Problems within Reinforcement Learning.
1:16:14 Learning vs. Planning. partial known environment vs. fully known environment.
1:20:38 Exploration vs. Exploitation.
1:24:25 Prediction vs. Control.
1:26:42 Course Overview
Just finished lecture 1 and can already tell this is going to be one of the absolute best courses 👌
I am a simple man. I see a great course, I press like
Wow, this is incredible. I'm currently going through Udacity and this lecture series blows their material from GT out of the water. Excellent examples, great explanation of theory, just wow. This actually helped me understand RL. THANK YOU!!!!!
How do you find the RL course from Udacity? Thinking about doing it after the DL Nanodegree.
@@JousefM I agree, those explanations by GT professors were confusing and less clear, the entire DS nanodegree which had ML, DL and RL was painful to watch and understand.
Right now I am sitting in my pajamas in the comfort of my home, eating a peanut butter and jelly sandwich and I have the ability to watch an entire course about an advanced topic online for free. What a time to be alive
1:10 Admin
6:13 About Reinforcement Learning
21:57 The Reinforcement Learning Problem
57:04 Inside an RL Agent
1:15:52 Problems within Reinforcement Learning
bless u :)
Thank you so much for repeating the questions each time.
Mr. Silver covers in 90 minutes what most books do not in 99 pages. Cheers and thanks!
I am taking a Deep Learning course rn but seriously the comments here are motivating me to get into this one right away.
This is a superb course on so many levels. Thank you
David is awesome at explaining a complex topic!. Great lecture. The examples really helped in understanding the concepts..
This is one of the clearest and most illuminating introductions I've watched on RL and its practical applications. Really looking forward to the following instalments.
I can say that I 've found a treasure..really
The lecturer is great!
I just finished the course, and the people in this comment section are not exaggerating. This is one of the best courses on Reinforcement learning. Thank you very much DeepMind, for making this free and available to everyone!
Excellent moment around 24:10 when David makes it crystal clear that there needs to be a metric to train by (better/worse) and that it's possible - and necessary - to try to come up with a scalar metric that roughly approximates success or failure in a field. When you train something to optimize for a metric, important to be clear up-front what that metric is.
the legend of all RL courses
Thanks a lot for the great lectures! I enjoyed watching every one of them (even #7). This is a great complement to reading Sutton/Barto and the seminal papers in RL.
I remember looking at the Atari paper in the late 2013 and having hard time to understand why everyone is going completely crazy about it. A few years later the trend was absolutely clear. Reinforcement Learning is the key to push the performance of AI systems past the threshold where the humans can serve as wise supervisors to the limit when the different kinds of intelligence help each other to improve via self-play.
I have never heard a punishment described as a negative reward at any point during my 71 orbits of the Sun. You can indeed learn something new every day.
I took this playlist as a reference for my thesis in "RL for green radio".
Rare opportunity to listen to Christian Bale after he is finished with dealing with criminals as Batman.
On a serious note, overall great series of lectures! Thanks, prof. David Silver!
His name should be David Gold or Platinum I dunno. Best intro to RL on YT, thank you!
Really love this video series. Watching it for the fifth time:)
Just completed Coursera's Reinforcement Learning Specialization and this is a nice addition to reinforce the concept I am learning.
lecture starts at 6:30
That was brilliant. Really helping me to get my head around the subject. Thanks David
I actually prefer this 2015 class over the most recent 2019 one. Nothing wrong on the other expositor, but David kinda makes the course more smoothly.
Can you enable the automatic captioning to this content?
Thank you for sharing your knowledge online. Enjoying your videos, and loving every minute of it.
Im back again, watching the whole video again.
This is really the best RL course I have seen!
Take swimming as example: learning is part that you directly jump into the water and learn swimming to survive; planning is that part that before jumping into the water, you read books/instructions on how to swim (obviously sometimes planning helps, sometimes not, sometimes counter-helps).
really high quality, I'm impressed at David Silver's (or somebody else's?) choice to offer this content to the general public free of charge.. what an age we're living in :DDDDDDDDDDD
@1:07:00. Instead of defining P_{ss'}^a and R_s^a, it's better to define p(s',r|s,a), which gives the joint probability of the new state and reward. The latter is the approach followed by the 2nd edition of Sutton&Barto's book.
It's really nice that he gives examples.
5 days to train per game. now is 5 minutes to complete a train based on recent papers. envolve fast!
David is awesome at explaining a complex topic!
Could you please turn on the auto generated subscript?
Plz..
As a learner of reinforcement learning, you should become an agent and improve yourself with getting rewards in this environment
I have to admit, david silver is slightly smarter than me.
RL could be the killer app in ML. Nice lectures to bring people up to speed!
For Me : David Silver is God ❤️ What a Man ! What an Explanation. One of the Greatest Minds who changed the Dynamics of RL in the past few years.Thanks Deep mind for uploading this Valuable course for free 🤍
Would be great if you guys could add subtitles!
Superb David - you are one of the giants I am standing on to see a little further - thank you
Silver is a boss.
The UCL lecturer is awesome. Thx for the excellent course.
Thank you for sharing.It kinda inspires me to always remember that I have to pass it on too.
This is my first taste of deep mind
Great lecture, great starting point. Helped me to understand the basics of Reinforcement Learning. Thanks for great content.
Thanks for a great lecture. I got grasp the point of reinforcement learning !
Thank you very much, I recently got a good grade in RL thanks to your great teaching skills!!
Great lecture. The examples really helped in understanding the concepts.
1:09:43 value function example in ternal
1:10:33 model; to the goal
Silver is Gold!
42:00 - 47:55 : Information State/Markov State
57:13 RL Agent
awesome course and awesome teacher
Amazing video, a very well-designed and well-delivered lecture! I'm going to enjoy this course, good job! 👍
if you can activate the subtitle from youtube, it will be great, Thanks
just amazing and different than any intro to RL
Great video. Thanks. I really needed something like this :)
Any observation and reward could be wrapped up into abstract data structure in an object for sorting.
His 2021 "Reward is Enough" paper makes us agree to the Reward Hypothesis @ 24:18 . :D
anyone can confirm if this is still relevant in 2022? I would like to study RL. It seems that there is a more recent series but with a different professor on this channel.
The future is independent of the past given the present
- David Silver
Only if it have Markov state
The above comment was meant to be in the context of your life. Your brain is a cumulative of all your prior experiences and the choices/decisions which you make will be an a action taken by your brain(which is a markov state). So what I perceived from that statement was that, "you need to forget your past and move on".
What a GIFT.
A really good introduction course!! Thank you very much!!
Excellent explanation. Thank you.
I want to discuss:
"All goals can be described by the maximisation of expected cumulative reward"
"Do you agree with this statement?"
My thoughts why it could be controversy is that you can never specify the reward such as you will never have unexpected side effects/behaviour of the agent.
Any other inputs/thoughts?
Excellent Indeed!
Thanks for the question at 19:48!
46:20 this also means that it doesn't matter how you got into this state, it will always mean the same.
Please add subtitles to make it more helpful for those who are from non English native countries
Ty for sharing, Great Lecture!!
Is it possible to add subtitles or autogenerated captions to these lecture videos?
We can't thank you enough!
The environment state(e,t) is Markov ... though it’s unknowable.
Amazing teacher I wish I could partecipate to this course! I did a course on Coursera but it was so quick to explain very complex things.
Are you referring to the RL specialization by Alberta university? If so, then how good was it on the programming/practical aspects?
@@pratikd5882 Yes, I did that. The exercises were good, but I'm not an AI guy but a simple programmer. I managed to do the exercises but I think that explainations were very concise. So in 15 minutes they explain what you get in 1 hour on those lectures. I think that is very summarized. But it's good they have exercises. So I don't think after doing that I'm actually able to do much
Do you have any suggestions about which one to start with , the Lecture series here or the RL specialization by Alberta University (on Coursera). I need to apply RL on my own project work. By the way I did the course on Machine learning by NG Andrews and I could follow the pace it was good enough for me and besides the programming exercises helped me alot than I could imagine. But I am not sure if so would be the case with RL by Coursera as well. Can you guide me on this?
amazing introduction and very cool
Begging for subtitles
Err have you disabled captions on this video?
O.o
Thank you very much!!
This is so good!
Captions would really help here!
imp point at 1:00:30
1:00:22 gamma's value is less than 1
Too good :) Best analogies.
Thanks so much!
Please enable the auto subtitle generator!
Thank you David!!
what was the bit about 15 hrtz ?
On Slide "History and State" @ 34:34, does the order of Actions, Observations and Rewards matter? If yes, then why the order isn't Observations, Rewards and Actions; the reasoning is that the agent sees the observations first, assesses the reward for actions and then takes a particular action? Please clarify if the chain-of-thought went awry at any place.
Thanks.
Thank you for nice lecture
brilliant course
Thanks very much. But I need Eng.subtitle. Could you change setting of this videos? :)
Lecture 1 has half a million views, 10 has 36k.
I'm surprised it's even 36k
Goated thanks g