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Advanced AI: Deep Reinforcement Learning in Python

The Complete Guide to Mastering Artificial Intelligence using Deep Learning and Neural Networks
Instructor:
Lazy Programmer Team
33,029 students enrolled
English [Auto] More
Build various deep learning agents (including DQN and A3C)
Apply a variety of advanced reinforcement learning algorithms to any problem
Q-Learning with Deep Neural Networks
Policy Gradient Methods with Neural Networks
Reinforcement Learning with RBF Networks
Use Convolutional Neural Networks with Deep Q-Learning

This course is all about the application of deep learning and neural networks to reinforcement learning.

If you’ve taken my first reinforcement learning class, then you know that reinforcement learning is on the bleeding edge of what we can do with AI.

Specifically, the combination of deep learning with reinforcement learning has led to AlphaGo beating a world champion in the strategy game Go, it has led to self-driving cars, and it has led to machines that can play video games at a superhuman level.

Reinforcement learning has been around since the 70s but none of this has been possible until now.

The world is changing at a very fast pace. The state of California is changing their regulations so that self-driving car companies can test their cars without a human in the car to supervise.

We’ve seen that reinforcement learning is an entirely different kind of machine learning than supervised and unsupervised learning.

Supervised and unsupervised machine learning algorithms are for analyzing and making predictions about data, whereas reinforcement learning is about training an agent to interact with an environment and maximize its reward.

Unlike supervised and unsupervised learning algorithms, reinforcement learning agents have an impetus – they want to reach a goal.

This is such a fascinating perspective, it can even make supervised / unsupervised machine learning and “data science” seem boring in hindsight. Why train a neural network to learn about the data in a database, when you can train a neural network to interact with the real-world?

While deep reinforcement learning and AI has a lot of potential, it also carries with it huge risk.

Bill Gates and Elon Musk have made public statements about some of the risks that AI poses to economic stability and even our existence.

As we learned in my first reinforcement learning course, one of the main principles of training reinforcement learning agents is that there are unintended consequences when training an AI.

AIs don’t think like humans, and so they come up with novel and non-intuitive solutions to reach their goals, often in ways that surprise domain experts – humans who are the best at what they do.

OpenAI is a non-profit founded by Elon Musk, Sam Altman (Y Combinator), and others, in order to ensure that AI progresses in a way that is beneficial, rather than harmful.

Part of the motivation behind OpenAI is the existential risk that AI poses to humans. They believe that open collaboration is one of the keys to mitigating that risk.

One of the great things about OpenAI is that they have a platform called the OpenAI Gym, which we’ll be making heavy use of in this course.

It allows anyone, anywhere in the world, to train their reinforcement learning agents in standard environments.

In this course, we’ll build upon what we did in the last course by working with more complex environments, specifically, those provided by the OpenAI Gym:

  • CartPole

  • Mountain Car

  • Atari games

To train effective learning agents, we’ll need new techniques.

We’ll extend our knowledge of temporal difference learning by looking at the TD Lambda algorithm, we’ll look at a special type of neural network called the RBF network, we’ll look at the policy gradient method, and we’ll end the course by looking at Deep Q-Learning (DQN) and A3C (Asynchronous Advantage Actor-Critic).

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:

  • College-level math is helpful (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 ANNs and CNNs in Theano or TensorFlow

  • Markov Decision Proccesses (MDPs)

  • Know how to implement Dynamic Programming, Monte Carlo, and Temporal Difference Learning to solve 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)

Introduction and Logistics

1
Introduction and Outline
2
Where to get the Code
3
How to Succeed in this Course
4
Tensorflow or Theano - Your Choice!

The Basics of Reinforcement Learning

1
Reinforcement Learning Section Introduction
2
Elements of a Reinforcement Learning Problem
3
States, Actions, Rewards, Policies
4
Markov Decision Processes (MDPs)
5
The Return
6
Value Functions and the Bellman Equation
7
What does it mean to “learn”?
8
Solving the Bellman Equation with Reinforcement Learning (pt 1)
9
Solving the Bellman Equation with Reinforcement Learning (pt 2)
10
Epsilon-Greedy
11
Q-Learning
12
How to Learn Reinforcement Learning
13
Suggestion Box

OpenAI Gym and Basic Reinforcement Learning Techniques

1
OpenAI Gym Tutorial
2
Random Search
3
Saving a Video
4
CartPole with Bins (Theory)
5
CartPole with Bins (Code)
6
RBF Neural Networks
7
RBF Networks with Mountain Car (Code)
8
RBF Networks with CartPole (Theory)
9
RBF Networks with CartPole (Code)
10
Theano Warmup
11
Tensorflow Warmup
12
Plugging in a Neural Network
13
OpenAI Gym Section Summary

TD Lambda

1
N-Step Methods
2
N-Step in Code
3
TD Lambda
4
TD Lambda in Code
5
TD Lambda Summary

Policy Gradients

1
Policy Gradient Methods
2
Policy Gradient in TensorFlow for CartPole
3
Policy Gradient in Theano for CartPole
4
Continuous Action Spaces
5
Mountain Car Continuous Specifics
6
Mountain Car Continuous Theano
7
Mountain Car Continuous Tensorflow
8
Mountain Car Continuous Tensorflow (v2)
9
Mountain Car Continuous Theano (v2)
10
Policy Gradient Section Summary

Deep Q-Learning

1
Deep Q-Learning Intro
2
Deep Q-Learning Techniques
3
Deep Q-Learning in Tensorflow for CartPole
4
Deep Q-Learning in Theano for CartPole
5
Additional Implementation Details for Atari
6
Pseudocode and Replay Memory
7
Deep Q-Learning in Tensorflow for Breakout
8
Deep Q-Learning in Theano for Breakout
9
Partially Observable MDPs
10
Deep Q-Learning Section Summary

A3C

1
A3C - Theory and Outline
2
A3C - Code pt 1 (Warmup)
3
A3C - Code pt 2
4
A3C - Code pt 3
5
A3C - Code pt 4
6
A3C - Section Summary
7
Course Summary

Theano and Tensorflow Basics Review

1
(Review) Theano Basics
2
(Review) Theano Neural Network in Code
3
(Review) Tensorflow Basics
4
(Review) Tensorflow Neural Network in Code

Setting Up Your Environment (FAQ by Student Request)

1
Windows-Focused Environment Setup 2018
2
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

Extra Help With Python Coding for Beginners (FAQ by Student Request)

1
How to Code by Yourself (part 1)
2
How to Code by Yourself (part 2)
3
Proof that using Jupyter Notebook is the same as not using it
4
Python 2 vs Python 3
5
Is Theano Dead?

Effective Learning Strategies for Machine Learning (FAQ by Student Request)

1
How to Succeed in this Course (Long Version)
2
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
3
Machine Learning and AI Prerequisite Roadmap (pt 1)
4
Machine Learning and AI Prerequisite Roadmap (pt 2)

Appendix / FAQ Finale

1
What is the Appendix?
2
BONUS: Where to get Udemy coupons and FREE deep learning material
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Advanced AI: Deep Reinforcement Learning in Python
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