ArtificialIntelligence_ReinforcementLear.epub - (EPUB全文下载)

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Artificial Intelligence: Reinforcement Learning in Python
By: The LazyProgrammer
 
Introduction
How to Succeed
What is Reinforcement Learning?
Where to get the Code
The Multi-Armed Bandit
Tic-Tac-Toe
Tic Tac Toe in Code
Markov Decision Processes
Dynamic Programming
Monte Carlo
Temporal Difference Learning
Function Approximation
Conclusion
 
Introduction
 
When people talk about artificial intelligence, they usually don’t mean supervised and unsupervised machine learning.
 
These tasks are pretty trivial compared to what we think of AIs doing - playing chess and Go, driving cars, and beating video games at a superhuman level.
 
Reinforcement learning has recently become popular for doing all of that and more.
 
Much like deep learning, a lot of the theory was discovered in the 70s and 80s but it hasn’t been until recently that we’ve been able to observe first hand the amazing results that are possible.
 
In 2016 we saw AlphaGo beat the world Champion in Go.
 
We saw AIs playing video games like Doom and Super Mario.
 
Self-driving cars have started driving on real roads with other drivers and even carrying passengers, all without human assistance.
 
If that sounds amazing, brace yourself for the future because the law of accelerating returns dictates that this progress is only going to continue to increase exponentially.
 
Yet learning about supervised and unsupervised machine learning is no small feat. To date I have over 16 courses just on those topics alone.
 
And still reinforcement learning opens up a whole new world. As you’ll learn in this book, the reinforcement learning paradigm is more different from supervised and unsupervised learning than they are from each other.
 
So what are we going to talk about in this book?
 
What I noticed in reinforcement learning is that there is a huge gap
between what you learn theory-wise and what you do during programming.
 
The theory is super abstract so it’s important to continuously think about how it actually applies to doing AI tasks like controlling a robot or playing a game.
 
So to that end, we are actually going to start
this book with a game: slot machines.
 
Slot machines, or the bandit problem, as it’s known in reinforcement learning, will introduce us to an important concept called the explore-exploit dilemma. You want to exploit the slot machine you believe is best, but you need to collect enough data from all the slot machines in order to be confident in that belief.
 
We’ll discuss several tec ............

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