Cutting-Edge AI: Deep Reinforcement Learning in Python

 

Description

Welcome
to Cutting-Edge AI!

 

This
is technically 
Deep Learning in
Python part 11 
of my deep learning series, and my 3rd reinforcement
learning course.

Deep
Reinforcement Learning is actually the combination of 2 topics: 
Reinforcement
Learning
 and Deep
Learning (Neural Networks)
.

While
both of these have been around for quite some time, it’s only been recently
that Deep Learning has really taken off, and along with it, Reinforcement
Learning.

The
maturation of deep learning has propelled advances in reinforcement learning,
which has been around since the 1980s, although some aspects of it, such as the
Bellman equation, have been for much longer.

 

Recently,
these advances have allowed us to showcase just how powerful reinforcement
learning can be.

We’ve
seen how 
AlphaZero can master the
game of Go using 
only self-play.

This
is just a few years after the original AlphaGo already beat a world champion in
Go.

 

We’ve
seen real-world robots learn how to walk, and even recover after being kicked
over, despite only being trained using simulation.

Simulation
is nice because it doesn’t require actual hardware, which is expensive. If your
agent falls down, no real damage is done.

 

We’ve
seen real-world robots learn hand dexterity, which is no small feat.

Walking
is one thing, but that involves coarse movements. Hand dexterity is complex –
you have many degrees of freedom and many of the forces involved are extremely
subtle.

Imagine
using your foot to do something you usually do with your hand, and you
immediately understand why this would be difficult.

 

Last
but not least – video games.

Even
just considering the past few months, we’ve seen some amazing developments. AIs
are now beating professional players in 
CS:GO and Dota
2
.

 

So
what makes this course different from the first two?

Now
that we know deep learning works with reinforcement learning, the question
becomes: how do we improve these algorithms?

This
course is going to show you a few different ways: including the powerful 
A2C
(Advantage Actor-Critic)
 algorithm, the DDPG
(Deep Deterministic Policy Gradient)
 algorithm,
and 
evolution strategies.

Evolution
strategies is a new and fresh take on reinforcement learning, that kind of
throws away all the old theory in favor of a more “black box”
approach, inspired by biological evolution.

 

What’s
also great about this new course is the variety of environments we get to look
at.

First,
we’re going to look at the classic 
Atari environments.
These are important because they show that reinforcement learning agents can
learn based on images alone.

Second,
we’re going to look at 
MuJoCo,
which is a physics simulator. This is the first step to building a robot that
can navigate the real-world and understand physics – we first have to show it
can work with simulated physics.

Finally,
we’re going to look at 
Flappy Bird,
everyone’s favorite mobile game just a few years ago.

 

Thanks
for reading, and I’ll see you in class!

 

“If
you can’t implement it, you don’t understand it”

  • Or as
    the great physicist Richard Feynman said: “What I cannot create, I do
    not understand”.
  • My
    courses are the ONLY courses where you will learn how to implement machine
    learning algorithms from scratch
  • Other
    courses will teach you how to plug in your data into a library, but do you
    really need help with 3 lines of code?
  • After
    doing the same thing with 10 datasets, you realize you didn’t learn 10
    things. You learned 1 thing, and just repeated the same 3 lines of code 10
    times…

 

Suggested
prerequisites:

  • Calculus
  • Probability
  • Object-oriented programming
  • Python
    coding: if/else, loops, lists, dicts, sets
  • Numpy
    coding: matrix and vector operations
  • Linear regression
  • Gradient descent
  • Know
    how to build a convolutional neural network (CNN) in TensorFlow
  • Markov Decision Proccesses (MDPs)

 

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

  • Check
    out the lecture “Machine Learning and AI Prerequisite
    Roadmap” (available in the FAQ of any of my courses, including the
    free Numpy course)

 


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content from: https://www.udemy.com/course/cutting-edge-artificial-intelligence

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