RL Course by David Silver - Lecture 1: Introduction to Reinforcement Learning

[@jordanburgess]

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.

[@Gabahulk]

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 :)

[@adarshmcool]

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.

[@TheAdithya1991]

Thanks for the review!

[@devonk298]

One of the best , if not the best , courses I've watched!

[@saltcheese]

thanks for the review

[@zingg7203]

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

[@차정민-b1z]

Good job. Very thankful :)

[@enochsit]

thanks

[@trdngy8230]

You made the world much easier! Thanks!

[@michaelc2406]

Problems within Reinforcement Learning 1:15:53

[@mairajamil001]

Thank you for this.

[@zhongchuxiong]

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

[@passerby4278]

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

[@tylersnard]

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.

[@DEVRAJ-np2og]

hlo, can u please suggeest roadmap for rl.

[@nguyenduy-sb4ue]

how lucky we are to have access to this kind of knowledge only with a button ! Thank you all in DeepMind public this course

[@BhuwanBhatta]

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.

[@sachinkalwar4359]

@@BhuwanBhatta fvy5tym 🎉4ufgc🙏😎4g🔥f9f4c6v f😎j 9c

[@anniekhoekzema9344]

@@BhuwanBhatta ji kghkfktghjkhhiljcujfjpjkui jikskjgjpj

[@tga3532]

The complete set of 10 lectures is brilliant. David's an excellent teacher. Highly recommended!

[@musicalwanderings7380]

Where can I find the rest of the lectures?

[@Abhi-wl5yt]

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!

[@socat9311]

I am a simple man. I see a great course, I press like

[@NganVu]

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

[@mathavraj9662]

bless u :)

[@ethanlyon8824]

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!!!!!

[@JousefM]

How do you find the RL course from Udacity? Thinking about doing it after the DL Nanodegree.

[@pratikd5882]

@@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.

[@eyeofhorus1301]

Just finished lecture 1 and can already tell this is going to be one of the absolute best courses 👌

[@tristanlouthrobins]

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.

[@ShalabhBhatnagar-vn4he]

Mr. Silver covers in 90 minutes what most books do not in 99 pages. Cheers and thanks!

[@Dynamyalo]

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

[@DrTune]

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.

[@vipulsharma3846]

I am taking a Deep Learning course rn but seriously the comments here are motivating me to get into this one right away.

[@elichen]

I'm really appreciating the intuitive style of this course, as contrasted to the Stanford course.

[@JustinArmstrong-u5w]

David is awesome at explaining a complex topic!. Great lecture. The examples really helped in understanding the concepts..

[@guupser]

Thank you so much for repeating the questions each time.

[@deviljin6217]

the legend of all RL courses

[@mgonetwo]

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!

[@vorushin]

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.

[@dbdg8405]

This is a superb course on so many levels. Thank you

[@rohitsaka]

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 🤍

[@yuwuxiong1165]

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).

[@nirajabcd]

Just completed Coursera's Reinforcement Learning Specialization and this is a nice addition to reinforce the concept I am learning.

[@lauriehartley9808]

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.

[@aam1819]

Thank you for sharing your knowledge online. Enjoying your videos, and loving every minute of it.

[@kiuhnmmnhuik2627]

@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.

[@linglingfan8138]

This is really the best RL course I have seen!

[@Newascap]

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.

[@TheAIEpiphany]

His name should be David Gold or Platinum I dunno. Best intro to RL on YT, thank you!

[@johntanchongmin]

Really love this video series. Watching it for the fifth time:)

[@mdoIsm771]

I took this playlist as a reference for my thesis in "RL for green radio".

[@AndreiMuntean0]

The lecturer is great!

[@hassan-ali-]

lecture starts at 6:30

[@saranggawane4719]

42:00 - 47:55 : Information State/Markov State 57:13 RL Agent

[@asavu]

David is awesome at explaining a complex topic!

[@wireghost897]

It's really nice that he gives examples.

[@Edin12n]

That was brilliant. Really helping me to get my head around the subject. Thanks David

[@Esaens]

Superb David - you are one of the giants I am standing on to see a little further - thank you

[@zhichaochen7732]

RL could be the killer app in ML. Nice lectures to bring people up to speed!

[@purnendumukherjee6876]

All the 10 lectures: kzbin.info/www/bejne/aKG6p2p9hNuonJI

[@tianmingdu8022]

The UCL lecturer is awesome. Thx for the excellent course.

[@ImtithalSaeed]

I can say that I 've found a treasure..really

[@iblaliftw]

Thank you very much, I recently got a good grade in RL thanks to your great teaching skills!!

[@alpsahin4340]

Great lecture, great starting point. Helped me to understand the basics of Reinforcement Learning. Thanks for great content.

[@43SunSon]

Im back again, watching the whole video again.

[@daaaniel21]

Thank you for sharing.It kinda inspires me to always remember that I have to pass it on too.

[@sng5192]

Thanks for a great lecture. I got grasp the point of reinforcement learning !

[@filippomiatto1289]

Amazing video, a very well-designed and well-delivered lecture! I'm going to enjoy this course, good job! 👍

[@yuxinzhang9403]

Any observation and reward could be wrapped up into abstract data structure in an object for sorting.

[@HazemAzim]

just amazing and different than any intro to RL

[@sachinramsuran7372]

Great lecture. The examples really helped in understanding the concepts.

[@Delta19G]

This is my first taste of deep mind

[@umountable]

46:20 this also means that it doesn't matter how you got into this state, it will always mean the same.

[@aj_shod]

Silver is Gold!

[@rossheaton7383]

Silver is a boss.

[@erichuang2009]

5 days to train per game. now is 5 minutes to complete a train based on recent papers. envolve fast!

[@AlessandroOrlandi83]

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.

[@pratikd5882]

Are you referring to the RL specialization by Alberta university? If so, then how good was it on the programming/practical aspects?

[@AlessandroOrlandi83]

@@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

[@satishrapol3650]

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?

[@bennog8902]

awesome course and awesome teacher

[@bocao3491]

Awesome! This is succinct and clarifies some concepts that I was confused of.

[@dalcimar]

Can you enable the automatic captioning to this content?

[@SphereofTime]

1:08:02 transition reward model

[@yehu7944]

Could you please turn on the auto generated subscript?

[@주동욱-l9j]

Plz..

[@Zebra745]

As a learner of reinforcement learning, you should become an agent and improve yourself with getting rewards in this environment

[@SphereofTime]

1:09:43 value function example in ternal

[@SphereofTime]

1:10:33 model; to the goal

[@SphereofTime]

13:27 reward delayed

[@mehershrishtinigam5449]

imp point at 1:00:30 1:00:22 gamma's value is less than 1

[@donamincorleone]

Great video. Thanks. I really needed something like this :)

[@taherhabib3180]

His 2021 "Reward is Enough" paper makes us agree to the Reward Hypothesis @ 24:18 . :D

[@43SunSon]

I have to admit, david silver is slightly smarter than me.

[@jamesr141]

What a GIFT.

[@_jiwi2674]

at 30:45, isn't the agent getting reward after taking action? It's not taking action based on the reward it receives

[@life42theuniverse]

The environment state(e,t) is Markov ... though it’s unknowable.

[@smilylife7515]

Please add subtitles to make it more helpful for those who are from non English native countries

[@lcswillems]

A really good introduction course!! Thank you very much!!

[@dashingrahulable]

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.

[@MGO2012]

Excellent explanation. Thank you.

[@timothdev]

Agent state is the internal representation of the agent, which is the summary of the past observations, used to decide on what action to make, given the observation from the environment. How then, at 51:19 the agent state = the observational state. I don't get this. Agent state is the internal representation isn't it. How it can be the input to the agent itself (observation state) ?

[@dantealexis7835]

At 51:19 he is speaking of when the agent can fully observe all variables in the environment, and presumably does not need to alter the representation of these variables. In such a case the agent state IS literally the environment state.

[@prashanthduvvuri7845]

The future is independent of the past given the present - David Silver

[@utsabshrestha277]

Only if it have Markov state

[@prashanthduvvuri7845]

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".

[@AhmedThabit99]

if you can activate the subtitle from youtube, it will be great, Thanks

[@florentinrieger5306]

This is so good!

[@AwesomeLemur]

We can't thank you enough!

[@abhijeetghodgaonkar]

Excellent Indeed!

[@ABHINAVGANDHI09]

Thanks for the question at 19:48!

[@kozzuli]

Ty for sharing, Great Lecture!!

[@84xyzabc]

isn't at 43:33 H_{1:t} should be H_{t:1}? inline with far-right expression of H.

[@pp-1954]

I wonder Markov property is the same as Newtonian mechanics in physics which is deterministic like Markov states. Newtonian mechanics say that if you know every momentum and acceleration of every particles existent, you can calculate/know the future 100%.

[@pp-1954]

Revision) If Markov state talks about the "instant" moment in time (delta t->0), I guess you cannot figure out the velocity or momentum so it is different from Newtonian.

[@ProfessionalTycoons]

amazing introduction and very cool

[@RahulSharma-yx5uf]

Thank you very much!!

[@legorative]

Too good :) Best analogies.

[@AntrianiStylianou]

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.

[@rz4413]

brilliant course

[@MimJim6784]

Please enable the auto subtitle generator!

[@aidan9876]

I found these psychologically useful. Are subtitles available? "The future is independent of the past ,given the present."

[@ahmadalghooneh2105]

14:00 - 14:47 is the reason why a mechanical engineer should know ML

[@shivramshetty5502]

Hi David, Thank you very much for such great video course. I would be very much appreciated if you help clear the following: 1. Is there a need for training by playing (hundreds of time) say the game as an example before being ready? if you have generalized program designed for reinforcement learning is it ready to play a new game who rules are different or need to be trained again. 2. There is mathematical proof that intermediate rewards can be calculated based on past experience - there is no need to know the intermediate rewards,. It can be derived say using deep mind Q function.- please correct - thank you. 3. In helicopter example current state depends on the past states like velocity acceleration - so not Markov 4. Can the reinforcement learning be realistically used to drive a car or run robot using generalized program that has inputs setup appropriately? Thank you, Sam

[@edmonddantes4705]

1 Hundreds of times is absolutely nothing in ML or RL. In order to learn to play games with a rich state space like go or chess, AlphaGo or AlphaZero can play billions of games. Also, by "generalised program" you mean architecture or algorithm, like algorithms based on Q-learning, concrete policy gradient methods, etc. If the rules are different, you can use the same algorithm if you reckon it is suitable, but you would have to train from scratch (unless there is a lot of similarity between the games and you can reuse some weights, as one can do with word embeddings in diverse NLP problems). 2 You are not being rigorous about the statement of your question, so it is very hard to understand what you exactly mean. It absolutely necessary to measure intermediate rewards. 3 How is the helicopter state not Markov? The helicopter movement is simulated by running a controlled ODE, and a numerical solver for an ODE is Markov by definition. Of course it is Markov. 4 Self-driving cars are an example of that.

[@wentingwang883]

Thanks so much!

[@vovos00]

Thank you for nice lecture

[@subrahmanyaswamyperuru2469]

Watching at 0.75X speed helps to follow better.