Reddit reviews Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning series)
We found 8 Reddit comments about Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning series). Here are the top ones, ranked by their Reddit score.
Bradford Book
I have a pretty wide probability distribution over the year for the first creation of superhuman AI, with a mode around 2060 (conditioning on no other existential catastrophes hitting us first). Many AI people predict superhuman AI sooner than this, though — including Rich Sutton, who quite literally wrote the book on reinforcement learning.
Once AI can drive cars better than humans can, then humanity will decide that driving cars was something that never required much "intelligence" in the first place, just like they did with chess. So I don't think driverless cars will cause people to believe that superhuman AI is coming soon — and it shouldn't, anyway.
When the military has fully autonomous battlefield robots, or a machine passes an in person Turing test, then people will start taking AI seriously.
Amusing note: Some military big-shots say things like "We'll never build fully-autonomous combat AIs; we'll never take humans out of the loop" (see Wired for War). Meanwhile, the U.S. military spends millions to get roboticist Ronald Arkin and his team to research and write the book Governing Lethal Behavior in Autonomous Robots. (One of the few serious works in the field of "machine ethics", BTW.)
Reading some books would be a good idea.
The following are textbooks:
General AI
Machine Learning
Statistics for Machine Learning
There are many other topics within AI which none of these books focus on, such as Natural Language Processing, Computer Vision, AI Alignment/Control/Ethics, and Philosophy of AI. libgen.io may be of great help to you.
Count me in!
I really want to read though this book: "https://www.amazon.com/Reinforcement-Learning-Introduction-Adaptive-Computation/dp/0262193981" by Richard Sutton, as well as a few other classical ML books, like Christopher Bishop's and Kevin Murphy's.
I know many concepts already, but I've never studied them in a systematic manner (e.g. follow an 1000-page book from end to end). I hear from multiple friends that it's super beneficial in the long run to build a strong mathematical/statistical foundation.
My current model of "googling here and there" might work in the short term, but will not help me invent new algorithms or improve state-of-the-art.
Read https://www.amazon.in/Reinforcement-Learning-Introduction-Richard-Sutton/dp/0262193981.
It's a great book for beginners in reinforcement learning.
If you're a lecture guy then watch deep mind's reinforcement learning lectures by David silver.
School of AI's move 37 course is also good.
Machine Learning, Mitchell
https://www.amazon.com/Machine-Learning-Tom-M-Mitchell/dp/0070428077
Reinforcement Learning, Sutton & Barto
https://www.amazon.com/Reinforcement-Learning-Introduction-Adaptive-Computation/dp/0262193981
Artificial Intelligence, Norvig & Russell
https://www.amazon.com/Artificial-Intelligence-Modern-Approach-3rd/dp/0136042597
The book that I followed and I think it's pretty standard is
https://www.amazon.com/Reinforcement-Learning-Introduction-Adaptive-Computation/dp/0262193981
Curated lists available on Github
https://github.com/muupan/deep-reinforcement-learning-papers
https://github.com/aikorea/awesome-rl
The deepmind website
https://deepmind.com/blog/deep-reinforcement-learning/
The above content is what I am familiar with. Perhaps there are better resources others can point toward.
the book you are looking for is Sutton and Barto's introduction to reinforcement learning. They have been involved in the space for decades, and have made meaningful contributions to the field. This is the beginner's text written by the masters. The math is surprisingly approachable considering. It begins with the multi-armed bandit problem... a problem so vexing, that in the 40's it was joked we needed to drop the problem proposition on Germany as a kind of logic bomb to distract them from the war efforts. The solution is a single equation that sits at the heart of modern reinforcement learning: the Bellman equation. It's a recursive, multivariate vector equation, so it can be challenging to wrap your head around at first, but it holds the key to understanding your way up into a lot of modern white papers even. Starting in a fairly simple, low dimensional version of the problem (the multi-armed bandit problem, then going up into markov decision processes) gives you a chance to build up some simple examples to hold in your head. How can you think about the Bellman equation in this really challenging videogame environment? Well... let's think back to tic tac toe. Let's think back to a Google Adwords campaign for maximizing sales on a short term seasonal promotion. Those simple examples will give you power, and this book is where to begin the work of etching those ideas in.
From there, the rest isn't too bad. If you also happen to have a good understanding of pytorch, python and deep learning, you'll be equipped to implement a lot of pretty cutting edge papers even. That'll be it's own learning journey, and you won't be ready for that leg until you're ready to start reading white papers in your free time. You'll get there too if you keep pushing, this is where you start. So yeah, definitely check that book out and see if your math is far enough to follow along. If it's not, then get a probability book instead or a vector calculus book or whatever it is you feel you're missing, and come back in six months. I've gone through a number of math books over the last two years, if you have a specific prerequisite you want to study, let me know... I might be able to point you towards another book instead, depending on what you need.
Sutton and Barto wrote the standard text in reinforcement learning.
Here is the AlphaGo paper in Nature.
The FAQ has a list of resources for learning ML including links to Hinton's Coursera course on neural nets.