Artificial Intelligence: Reinforcement Learning in Python
Complete guide to Reinforcement Learning, with Stock Trading and Online Advertising Applications
Created by Lazy Programmer Team  Artificial Intelligence and Machine Learning Engineer
Students: 41189, Price: $199.99
Students: 41189, Price: Paid
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 Google’s AlphaGo beat the world Champion in Go.
We saw AIs playing video games like Doom and Super Mario.
Selfdriving cars have started driving on real roads with other drivers and even carrying passengers (Uber), 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.
Learning about supervised and unsupervised machine learning is no small feat. To date I have over TWENTY FIVE (25!) courses just on those topics alone.
And yet reinforcement learning opens up a whole new world. As you’ll learn in this course, the reinforcement learning paradigm is very from both supervised and unsupervised learning.
It’s led to new and amazing insights both in behavioral psychology and neuroscience. As you’ll learn in this course, there are many analogous processes when it comes to teaching an agent and teaching an animal or even a human. It’s the closest thing we have so far to a true artificial general intelligence. What’s covered in this course?

The multiarmed bandit problem and the exploreexploit dilemma

Ways to calculate means and moving averages and their relationship to stochastic gradient descent

Markov Decision Processes (MDPs)

Dynamic Programming

Monte Carlo

Temporal Difference (TD) Learning (QLearning and SARSA)

Approximation Methods (i.e. how to plug in a deep neural network or other differentiable model into your RL algorithm)

How to use OpenAI Gym, with zero code changes

Project: Apply QLearning to build a stock trading bot
If you’re ready to take on a brand new challenge, and learn about AI techniques that you’ve never seen before in traditional supervised machine learning, unsupervised machine learning, or even deep learning, then this course is for you.
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

Objectoriented programming

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: matrix and vector operations

Linear regression

Gradient descent
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)
Advanced AI: Deep Reinforcement Learning in Python
The Complete Guide to Mastering Artificial Intelligence using Deep Learning and Neural Networks
Created by Lazy Programmer Team  Artificial Intelligence and Machine Learning Engineer
Students: 33375, Price: $29.99
Students: 33375, Price: Paid
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 selfdriving 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 selfdriving 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 realworld?
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 nonintuitive solutions to reach their goals, often in ways that surprise domain experts  humans who are the best at what they do.
OpenAI is a nonprofit 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 QLearning (DQN) and A3C (Asynchronous Advantage ActorCritic).
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:

Collegelevel math is helpful (calculus, probability)

Objectoriented 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)
Tensorflow 2.0: Deep Learning and Artificial Intelligence
Neural Networks for Computer Vision, Time Series Forecasting, NLP, GANs, Reinforcement Learning, and More!
Created by Lazy Programmer Inc.  Artificial intelligence and machine learning engineer
Students: 31072, Price: $129.99
Students: 31072, Price: Paid
Welcome to Tensorflow 2.0!
What an exciting time. It's been nearly 4 years since Tensorflow was released, and the library has evolved to its official second version.
Tensorflow is Google's library for deep learning and artificial intelligence.
Deep Learning has been responsible for some amazing achievements recently, such as:

Generating beautiful, photorealistic images of people and things that never existed (GANs)

Beating world champions in the strategy game Go, and complex video games like CS:GO and Dota 2 (Deep Reinforcement Learning)

Selfdriving cars (Computer Vision)

Speech recognition (e.g. Siri) and machine translation (Natural Language Processing)

Even creating videos of people doing and saying things they never did (DeepFakes  a potentially nefarious application of deep learning)
Tensorflow is the world's most popular library for deep learning, and it's built by Google, whose parent Alphabet recently became the most cashrich company in the world (just a few days before I wrote this). It is the library of choice for many companies doing AI and machine learning.
In other words, if you want to do deep learning, you gotta know Tensorflow.
This course is for beginnerlevel students all the way up to expertlevel students. How can this be?
If you've just taken my free Numpy prerequisite, then you know everything you need to jump right in. We will start with some very basic machine learning models and advance to state of the art concepts.
Along the way, you will learn about all of the major deep learning architectures, such as Deep Neural Networks, Convolutional Neural Networks (image processing), and Recurrent Neural Networks (sequence data).
Current projects include:

Natural Language Processing (NLP)

Recommender Systems

Transfer Learning for Computer Vision

Generative Adversarial Networks (GANs)

Deep Reinforcement Learning Stock Trading Bot
Even if you've taken all of my previous courses already, you will still learn about how to convert your previous code so that it uses Tensorflow 2.0, and there are allnew and neverbeforeseen projects in this course such as time series forecasting and how to do stock predictions.
This course is designed for students who want to learn fast, but there are also "indepth" sections in case you want to dig a little deeper into the theory (like what is a loss function, and what are the different types of gradient descent approaches).
Advanced Tensorflow topics include:

Deploying a model with Tensorflow Serving (Tensorflow in the cloud)

Deploying a model with Tensorflow Lite (mobile and embedded applications)

Distributed Tensorflow training with Distribution Strategies

Writing your own custom Tensorflow model

Converting Tensorflow 1.x code to Tensorflow 2.0

Constants, Variables, and Tensors

Eager execution

Gradient tape
Instructor's Note: This course focuses on breadth rather than depth, with less theory in favor of building more cool stuff. If you are looking for a more theorydense course, this is not it. Generally, for each of these topics (recommender systems, natural language processing, reinforcement learning, computer vision, GANs, etc.) I already have courses singularly focused on those topics.
Thanks for reading, and I’ll see you in class!
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)
Practical Reinforcement Learning using Python – 8 AI Agents
Use CuttingEdge Reinforcement Learning algorithms in Environments like Flappy Bird, Mario, Stocks and Much More!!
Created by Samuel BoylanSajous  Developer & Teacher
Students: 25167, Price: $19.99
Students: 25167, Price: Paid
Join the most comprehensive Reinforcement Learning course on Udemy and learn how to build Amazing Reinforcement Learning Applications!
Do you want to learn how to build cutting edge trading algorithms that leverage todays technology? Or do you want to learn the tools and skills that are considered the state of the art of Artificial Intelligence? Or do you just want to learn Reinforcement Learning in a Highly practical way?
After completing this course you will be able to:

Build any reinforcement learning algorithm in any environment

Use Reinforcement Learning for your own scientific experiments

Solve problems using Reinforcement Learning

Leverage Cutting Edge Technologies for your own project

Master OpenAI gym's
Why should you choose this course?
This course guides you through a stepbystep process of building state of the art trading algorithms and ensures that you walk away with the practical skills to build any reinforcement learning algorithm idea you have and implement it efficiently.
Here's what's included in the course:

Atari Reinforcement Learning Agent

Build QLearning from scratch and implement it in Autonomous Taxi Environment

Build Deep QLearning from scratch and implement it in Flappy Bird

Build Deep QLearning from scratch and implement it in Mario

Build a Stock Reinforcement Learning Algorithm

Build a intelligent car that can complete various environments

And much more!
This course is for you if ...

You're interested in cutting edge technology and applying it in practical ways

You're passionate about Deep Learning/AI

Want to learn about cuttingedge technologies!

Want to learn reinforcement learning by doing cool projects!
Course prerequisites:

Python!
CuttingEdge AI: Deep Reinforcement Learning in Python
Apply deep learning to artificial intelligence and reinforcement learning using evolution strategies, A2C, and DDPG
Created by Lazy Programmer Inc.  Artificial intelligence and machine learning engineer
Students: 22621, Price: $109.99
Students: 22621, Price: Paid
Welcome to CuttingEdge 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 selfplay.
This is just a few years after the original AlphaGo already beat a world champion in Go.
We’ve seen realworld 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 realworld 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 ActorCritic) 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 realworld 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

Objectoriented 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)
Deep Reinforcement Learning: Handson AI Tutorial in Python
Develop Artificial Intelligence Applications using Reinforcement Learning in Python.
Created by Mehdi Mohammadi  Machine Learning Engineer
Students: 16307, Price: $89.99
Students: 16307, Price: Paid
In this course we learn the concepts and fundamentals of reinforcement learning, it's relation to artificial intelligence and machine learning, and how we can formulate a problem in the context of reinforcement learning and Markov Decision Process. We cover different fundamental algorithms including QLearning, SARSA as well as Deep QLearning. We present the whole implementation of two projects from scratch with Qlearning and Deep QNetwork.
[2021] Machine Learning and Deep Learning Bootcamp in Python
Machine Learning models, Neural Networks, Deep Learning and Reinforcement Learning Approaches in Keras and TensorFlow
Created by Holczer Balazs  Software Engineer
Students: 7631, Price: $119.99
Students: 7631, Price: Paid
This course is about the fundamental concepts of machine learning, focusing on regression, SVM, decision trees and neural networks. These topics are getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help detect cancer for example or we may construct algorithms that can have a very good guess about stock prices movement in the market.
In each section we will talk about the theoretical background for all of these algorithms then we are going to implement these problems together. We will use Python with SkLearn, Keras and TensorFlow.

Machine Learning Algorithms: machine learning approaches are becoming more and more important even in 2020. In this course, you can learn about:

linear regression model

logistic regression model

k nearest neighbour classifier

naive Bayes classifier

support vector machines (SVMs)

random forest classifier

boosting algorithm

principle components analysis (PCA)


Machine Learning approaches in finance: how to use learning algorithms to predict stock prices

Computer Vision and Face Detection with OpenCV

Neural Networks: what are feedforward neural networks and why are they useful

Deep Learning: feedforward neural networks and deep neural networks are the stateoftheart approaches in artificial intelligence in 2020. So what are the topics you will learn in this course?

deep neural networks

convolutional neural networks (CNNs)

recurrent neural networks (RNNs)


Recurrent Neural Networks and Convolutional Neural Networks and their applications such as sentiment analysis or stock prices forecast

Reinforcement Learning: Markov Decision processes (MDPs) and Qlearning

Tic Tac Toe game with Q learning approach and the deep Q learning approach
Thanks for joining the course, let's get started!
Deep Reinforcement Learning 2.0
The smartest combination of Deep QLearning, Policy Gradient, Actor Critic, and DDPG
Created by Hadelin de Ponteves  AI Entrepreneur
Students: 7230, Price: $109.99
Students: 7230, Price: Paid
Welcome to Deep Reinforcement Learning 2.0!
In this course, we will learn and implement a new incredibly smart AI model, called the TwinDelayed DDPG, which combines state of the art techniques in Artificial Intelligence including continuous Double Deep QLearning, Policy Gradient, and Actor Critic. The model is so strong that for the first time in our courses, we are able to solve the most challenging virtual AI applications (training an ant/spider and a half humanoid to walk and run across a field).
To approach this model the right way, we structured the course in three parts:

Part 1: Fundamentals
In this part we will study all the fundamentals of Artificial Intelligence which will allow you to understand and master the AI of this course. These include QLearning, Deep QLearning, Policy Gradient, ActorCritic and more. 
Part 2: The TwinDelayed DDPG Theory
We will study in depth the whole theory behind the model. You will clearly see the whole construction and training process of the AI through a series of clear visualization slides. Not only will you learn the theory in details, but also you will shape up a strong intuition of how the AI learns and works. The fundamentals in Part 1, combined to the very detailed theory of Part 2, will make this highly advanced model accessible to you, and you will eventually be one of the very few people who can master this model. 
Part 3: The TwinDelayed DDPG Implementation
We will implement the model from scratch, step by step, and through interactive sessions, a new feature of this course which will have you practice on many coding exercises while we implement the model. By doing them you will not follow passively the course but very actively, therefore allowing you to effectively improve your skills. And last but not least, we will do the whole implementation on Colaboratory, or Google Colab, which is a totally free and open source AI platform allowing you to code and train some AIs without having any packages to install on your machine. In other words, you can be 100% confident that you press the execute button, the AI will start to train and you will get the videos of the spider and humanoid running in the end.
PyTorch: Deep Learning and Artificial Intelligence
Neural Networks for Computer Vision, Time Series Forecasting, NLP, GANs, Reinforcement Learning, and More!
Created by Lazy Programmer Team  Artificial Intelligence and Machine Learning Engineer
Students: 4312, Price: $199.99
Students: 4312, Price: Paid
Welcome to PyTorch: Deep Learning and Artificial Intelligence!
Although Google's Deep Learning library Tensorflow has gained massive popularity over the past few years, PyTorch has been the library of choice for professionals and researchers around the globe for deep learning and artificial intelligence.
Is it possible that Tensorflow is popular only because Google is popular and used effective marketing?
Why did Tensorflow change so significantly between version 1 and version 2? Was there something deeply flawed with it, and are there still potential problems?
It is less wellknown that PyTorch is backed by another Internet giant, Facebook (specifically, the Facebook AI Research Lab  FAIR). So if you want a popular deep learning library backed by billion dollar companies and lots of community support, you can't go wrong with PyTorch. And maybe it's a bonus that the library won't completely ruin all your old code when it advances to the next version. ;)
On the flip side, it is very wellknown that all the top AI shops (ex. OpenAI, Apple, and JPMorgan Chase) use PyTorch. OpenAI just recently switched to PyTorch in 2020, a strong sign that PyTorch is picking up steam.
If you are a professional, you will quickly recognize that building and testing new ideas is extremely easy with PyTorch, while it can be pretty hard in other libraries that try to do everything for you. Oh, and it's faster.
Deep Learning has been responsible for some amazing achievements recently, such as:

Generating beautiful, photorealistic images of people and things that never existed (GANs)

Beating world champions in the strategy game Go, and complex video games like CS:GO and Dota 2 (Deep Reinforcement Learning)

Selfdriving cars (Computer Vision)

Speech recognition (e.g. Siri) and machine translation (Natural Language Processing)

Even creating videos of people doing and saying things they never did (DeepFakes  a potentially nefarious application of deep learning)
This course is for beginnerlevel students all the way up to expertlevel students. How can this be?
If you've just taken my free Numpy prerequisite, then you know everything you need to jump right in. We will start with some very basic machine learning models and advance to state of the art concepts.
Along the way, you will learn about all of the major deep learning architectures, such as Deep Neural Networks, Convolutional Neural Networks (image processing), and Recurrent Neural Networks (sequence data).
Current projects include:

Natural Language Processing (NLP)

Recommender Systems

Transfer Learning for Computer Vision

Generative Adversarial Networks (GANs)

Deep Reinforcement Learning Stock Trading Bot
Even if you've taken all of my previous courses already, you will still learn about how to convert your previous code so that it uses PyTorch, and there are allnew and neverbeforeseen projects in this course such as time series forecasting and how to do stock predictions.
This course is designed for students who want to learn fast, but there are also "indepth" sections in case you want to dig a little deeper into the theory (like what is a loss function, and what are the different types of gradient descent approaches).
I'm taking the approach that even if you are not 100% comfortable with the mathematical concepts, you can still do this! In this course, we focus more on the PyTorch library, rather than deriving any mathematical equations. I have tons of courses for that already, so there is no need to repeat that here.
Instructor's Note: This course focuses on breadth rather than depth, with less theory in favor of building more cool stuff. If you are looking for a more theorydense course, this is not it. Generally, for each of these topics (recommender systems, natural language processing, reinforcement learning, computer vision, GANs, etc.) I already have courses singularly focused on those topics.
Thanks for reading, and I’ll see you in class!
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)
Practical AI with Python and Reinforcement Learning
Learn how to use Reinforcement Learning techniques to create practical Artificial Intelligence programs!
Created by Jose Portilla  Head of Data Science, Pierian Data Inc.
Students: 3177, Price: $89.99
Students: 3177, Price: Paid
Please note! This course is in an "early bird" release, and we're still updating and adding content to it, please keep in mind before enrolling that the course is not yet complete.
“The future is already here – it’s just not very evenly distributed.“
Have you ever wondered how Artificial Intelligence actually works? Do you want to be able to harness the power of neural networks and reinforcement learning to create intelligent agents that can solve tasks with human level complexity?
This is the ultimate course online for learning how to use Python to harness the power of Neural Networks to create Artificially Intelligent agents!
This course focuses on a practical approach that puts you in the driver's seat to actually build and create intelligent agents, instead of just showing you small toy examples like many other online courses. Here we focus on giving you the power to apply artificial intelligence to your own problems, environments, and situations, not just those included in a niche library!
This course covers the following topics:

Artificial Neural Networks

Convolution Neural Networks

Classical QLearning

Deep QLearning

SARSA

Cross Entropy Methods

Double DQN

and much more!
We've designed this course to get you to be able to create your own deep reinforcement learning agents on your own environments. It focuses on a practical approach with the right balance of theory and intuition with useable code. The course uses clear examples in slides to connect mathematical equations to practical code implementation, before showing how to manually implement the equations that conduct reinforcement learning.
We'll first show you how Deep Learning with Keras and TensorFlow works, before diving into Reinforcement Learning concepts, such as QLearning. Then we can combine these ideas to walk you through Deep Reinforcement Learning agents, such as Deep QNetworks!
There is still a lot more to come, I hope you'll join us inside the course!
Jose
Modern Reinforcement Learning: Deep Q Learning in PyTorch
How to Turn Deep Reinforcement Learning Research Papers Into Agents That Beat Classic Atari Games
Created by Phil Tabor  Machine Learning Engineer
Students: 3053, Price: $99.99
Students: 3053, Price: Paid
In this complete deep reinforcement learning course you will learn a repeatable framework for reading and implementing deep reinforcement learning research papers. You will read the original papers that introduced the Deep Q learning, Double Deep Q learning, and Dueling Deep Q learning algorithms. You will then learn how to implement these in pythonic and concise PyTorch code, that can be extended to include any future deep Q learning algorithms. These algorithms will be used to solve a variety of environments from the Open AI gym's Atari library, including Pong, Breakout, and Bankheist.
You will learn the key to making these Deep Q Learning algorithms work, which is how to modify the Open AI Gym's Atari library to meet the specifications of the original Deep Q Learning papers. You will learn how to:

Repeat actions to reduce computational overhead

Rescale the Atari screen images to increase efficiency

Stack frames to give the Deep Q agent a sense of motion

Evaluate the Deep Q agent's performance with random noops to deal with model over training

Clip rewards to enable the Deep Q learning agent to generalize across Atari games with different score scales
If you do not have prior experience in reinforcement or deep reinforcement learning, that's no problem. Included in the course is a complete and concise course on the fundamentals of reinforcement learning. The introductory course in reinforcement learning will be taught in the context of solving the Frozen Lake environment from the Open AI Gym.
We will cover:

Markov decision processes

Temporal difference learning

The original Q learning algorithm

How to solve the Bellman equation

Value functions and action value functions

Model free vs. model based reinforcement learning

Solutions to the exploreexploit dilemma, including optimistic initial values and epsilongreedy action selection
Also included is a mini course in deep learning using the PyTorch framework. This is geared for students who are familiar with the basic concepts of deep learning, but not the specifics, or those who are comfortable with deep learning in another framework, such as Tensorflow or Keras. You will learn how to code a deep neural network in Pytorch as well as how convolutional neural networks function. This will be put to use in implementing a naive Deep Q learning agent to solve the Cartpole problem from the Open AI gym.
Reinforcement Learning with Pytorch
Learn to apply Reinforcement Learning and Artificial Intelligence algorithms using Python, Pytorch and OpenAI Gym
Created by Atamai AI Team  Data Science & AI Passion
Students: 2394, Price: $109.99
Students: 2394, Price: Paid
UPDATE:
All the code and installation instructions have been updated and verified to work with Pytorch 1.6 !!
Artificial Intelligence is dynamically edging its way into our lives. It is already broadly available and we use it  sometimes even not knowing it  on daily basis. Soon it will be our permanent, every day companion.
And where can we place Reinforcement Learning in AI world? Definitely this is one of the most promising and fastest growing technologies that can eventually lead us to General Artificial Intelligence! We can see multiple examples where AI can achieve amazing results  from reaching super human level while playing games to solving real life problems (robotics, healthcare, etc).
Without a doubt it's worth to know and understand it!
And that's why this course has been created.
We will go through multiple topics, focusing on most important and practical details. We will start from very basic information, gradually building our understanding, and finally reaching the point where we will make our agent learn in humanlike way  only from video input!
What's important  of course we need to cover some theory  but we will mainly focus on practical part. Goal is to understand WHY and HOW.
In order to evaluate our algorithms we will use environments from  very popular  OpenAI Gym. We will start from basic text games, through more complex ones, up to challenging Atari games
What will be covered during the course ?
 Introduction to Reinforcement Learning
 Markov Decision Process
 Deterministic and stochastic environments
 Bellman Equation
 Q Learning
 Exploration vs Exploitation
 Scaling up
 Neural Networks as function approximators
 Deep Reinforcement Learning
 DQN
 Improvements to DQN
 Learning from video input
 Reproducing some of most popular RL solutions
 Tuning parameters and general recommendations
See you in the class!
Artificial Intelligence for Simple Games
Learn how to use powerful Deep Reinforcement Learning and Artificial Intelligence tools on examples of AI simple games!
Created by Jan Warchocki  Artificial Intelligence Engineer
Students: 1578, Price: $99.99
Students: 1578, Price: Paid
Ever wish you could harness the power of Deep Learning and Machine Learning to craft intelligent bots built for gaming?
If you’re looking for a creative way to dive into Artificial Intelligence, then ‘Artificial Intelligence for Simple Games’ is your key to building lasting knowledge.
Learn and test your AI knowledge of fundamental DL and ML algorithms using the fun and flexible environment of simple games such as Snake, the Travelling Salesman problem, mazes and more.
1. Whether you’re an absolute beginner or seasoned Machine Learning expert, this course provides a solid foundation of the basic and advanced concepts you need to build AI within a gaming environment and beyond.
2. Key algorithms and concepts covered in this course include: Genetic Algorithms, QLearning, Deep QLearning with both Artificial Neural Networks and Convolutional Neural Networks.
3. Dive into SuperDataScience’s muchloved, interactive learning environment designed to build knowledge and intuition gradually with practical, yet challenging case studies.
4. Code flexibility means that students will be able to experiment with different game scenarios and easily apply their learning to business problems outside of the gaming industry.
‘AI for Simple Games’ Curriculum
Section #1 — Dive into Genetic Algorithms by applying the famous Travelling Salesman Problem to an intergalactic game. The challenge will be to build a spaceship that travels across all planets in the shortest time possible!
Section #2 — Learn the foundations of the modelfree reinforcement learning algorithm, QLearning. Develop intuition and visualization skills, and try your hand at building a custom maze and design an AI able to find its way out.
Section #3 — Go deep with Deep QLearning. Explore the fantastic world of Neural Networks using the OpenAI Gym development environment and learn how to build AIs for many other simple games!
Section #4 — Finish off the course by building your very own version of the classic game, Snake! Here you’ll utilize Convolutional Neural Networks by building an AI that mimics the same behavior we see when playing Snake.
Modern Reinforcement Learning: ActorCritic Algorithms
How to Implement Cutting Edge Artificial Intelligence Research Papers in the Open AI Gym Using the PyTorch Framework
Created by Phil Tabor  Machine Learning Engineer
Students: 1458, Price: $99.99
Students: 1458, Price: Paid
In this advanced course on deep reinforcement learning, you will learn how to implement policy gradient, actor critic, deep deterministic policy gradient (DDPG), twin delayed deep deterministic policy gradient (TD3), and soft actor critic (SAC) algorithms in a variety of challenging environments from the Open AI gym. There will be a strong focus on dealing with environments with continuous action spaces, which is of particular interest for those looking to do research into robotic control with deep reinforcement learning.
Rather than being a course that spoon feeds the student, here you are going to learn to read deep reinforcement learning research papers on your own, and implement them from scratch. You will learn a repeatable framework for quickly implementing the algorithms in advanced research papers. Mastering the content in this course will be a quantum leap in your capabilities as an artificial intelligence engineer, and will put you in a league of your own among students who are reliant on others to break down complex ideas for them.
Fear not, if it's been a while since your last reinforcement learning course, we will begin with a briskly paced review of core topics.
The course begins with a practical review of the fundamentals of reinforcement learning, including topics such as:

The Bellman Equation

Markov Decision Processes

Monte Carlo Prediction

Monte Carlo Control

Temporal Difference Prediction TD(0)

Temporal Difference Control with Q Learning
And moves straight into coding up our first agent: a blackjack playing artificial intelligence. From there we will progress to teaching an agent to balance the cart pole using Q learning.
After mastering the fundamentals, the pace quickens, and we move straight into an introduction to policy gradient methods. We cover the REINFORCE algorithm, and use it to teach an artificial intelligence to land on the moon in the lunar lander environment from the Open AI gym. Next we progress to coding up the one step actor critic algorithm, to again beat the lunar lander.
With the fundamentals out of the way, we move on to our harder projects: implementing deep reinforcement learning research papers. We will start with Deep Deterministic Policy Gradients (DDPG), which is an algorithm for teaching robots to excel at a variety of continuous control tasks. DDPG combines many of the advances of Deep Q Learning with traditional actor critic methods to achieve state of the art results in environments with continuous action spaces.
Next, we implement a state of the art artificial intelligence algorithm: Twin Delayed Deep Deterministic Policy Gradients (TD3). This algorithm sets a new benchmark for performance in continuous robotic control tasks, and we will demonstrate world class performance in the Bipedal Walker environment from the Open AI gym. TD3 is based on the DDPG algorithm, but addresses a number of approximation issues that result in poor performance in DDPG and other actor critic algorithms.
Finally, we will implement the soft actor critic algorithm (SAC). SAC approaches deep reinforcement learning from a totally different angle: by considering entropy maximization, rather than score maximization, as a viable objective. This results in increased exploration by our agent, and world class performance in a number of important Open AI Gym environments.
By the end of the course, you will know the answers to the following fundamental questions in ActorCritic methods:

Why should we bother with actor critic methods when deep Q learning is so successful?

Can the advances in deep Q learning be used in other fields of reinforcement learning?

How can we solve the exploreexploit dilemma with a deterministic policy?

How do we get and deal with overestimation bias in actorcritic methods?

How do we deal with the inherent approximation errors in deep neural networks?
This course is for the highly motivated and advanced student. To succeed, you must have prior course work in all the following topics:

College level calculus

Reinforcement learning

Deep learning
The pace of the course is brisk and the topics are at the cutting edge of deep reinforcement learning research, but the payoff is that you will come out knowing how to read research papers and turn them into functional code as quickly as possible. You'll never have to rely on dodgy medium blog posts again.
Artificial Intelligence IV – Reinforcement Learning in Java
All you need to know about Markov Decision processes, value and policyiteation as well as about Q learning approach
Created by Holczer Balazs  Software Engineer
Students: 1376, Price: $89.99
Students: 1376, Price: Paid
This course is about Reinforcement Learning. The first step is to talk about the mathematical background: we can use a Markov Decision Process as a model for reinforcement learning. We can solve the problem 3 ways: valueiteration, policyiteration and Qlearning. Qlearning is a model free approach so it is stateoftheart approach. It learns the optimal policy by interacting with the environment. So these are the topics:
 Markov Decision Processes
 valueiteration and policyiteration
 Qlearning fundamentals
 pathfinding algorithms with Qlearning
 Qlearning with neural networks
Reinforcement Learning: AI Flight with Unity MLAgents
Teach airplanes to fly with Unity's Reinforcement Learning platform
Created by Adam Kelly Immersive Limit  3D DEVELOPMENT + DEEP LEARNING
Students: 804, Price: $59.99
Students: 804, Price: Paid
Interested in the intersection of video games and artificial intelligence? If so, you will love Unity MLAgents.
Reinforcement Learning with MLAgents is naturally more intuitive than other machine learning approaches because you can watch your neural network learn in a realtime 3d environment based on rewards for good behavior. It's more fun because you can easily apply it to your own video game ideas rather than working with simplified example problems in a library like OpenAI Gym.
In this course, we will create a complete game with incredibly challenging AI opponents.

We'll start with an introduction to MLAgents, including how to use and train the example content.

Then, we'll use Blender to make custom assets for our game (you can skip that part if you just want to code).

Next, we'll create a full environment for the airplane agents and train them to fly through checkpoints without crashing into obstacles.

Finally, we'll take our trained agents and build a full game around them that you can play, including menus for level and difficulty selection.
Important note 1: We DO NOT cover the foundations of deep learning or reinforcement learning in this course. We will focus on how to use MLAgents, which abstracts the hard stuff and allows us to focus on building our training environment and crafting rewards.
Important note 2: While the course was originally recorded with MLAgents version 0.11, we have updated it for version 1.0.
As you work through the course, you'll have plenty of opportunities to customize it and make it your own. At the end, you'll have a complete game that you can share with friends, add to your portfolio, or sell on a game marketplace.
Machine Learning: Beginner Reinforcement Learning in Python
How to teach a neural network to play a game using delayed gratification in 146 lines of Python code
Created by Milo SpencerHarper  Software Engineer
Students: 385, Price: $24.99
Students: 385, Price: Paid
This course is designed for beginners to machine learning. Some of the most exciting advances in artificial intelligence have occurred by challenging neural networks to play games. I will introduce the concept of reinforcement learning, by teaching you to code a neural network in Python capable of delayed gratification.
We will use the NChain game provided by the Open AI institute. The computer gets a small reward if it goes backwards, but if it learns to make short term sacrifices by persistently pressing forwards it can earn a much larger reward. Using this example I will teach you Deep Q Learning  a revolutionary technique invented by Google DeepMind to teach neural networks to play chess, Go and Atari.
Comprehensive Guide to Artificial Intelligence(AI) for All
Learn ML, NLP, Deep, Transfer and Reinforcement learning with IBM Watson, Tensorflow Sim, Keras, OpenAI Gym and more
Created by Junaid Ahmed  Technology Author, Leader, Strategist and Trainer
Students: 299, Price: $89.99
Students: 299, Price: Paid
If I can tell you, stop what ever you are doing and do a certain thing. I would say "Learn about AI and the impact it is going to have in your professional life, personal life and much more in the immediate future".
Welcome to this exciting and eye opening course on Artificial Intelligence and more. We believe that AI will touch everybody in some level, whether you are a technical or a non technical person and also that you can excel in many roles in AI with just a functional understanding of coding.
The course has over 11 hours of content with 100+ easy to consume, high quality, visually engaging, condensed and edited videos, over 10 Quizzes to check your understanding, reference material and code for further study.
This course has 3 parts, first we will start from the basics , break myths, clarify your understanding as to what is this mysterious term AI, (many are surprised to know that it encompasses, Machine Learning, NLP,Computer Vision, IOT, Robotics and more). We will also understand the current state of AI and its positive and negative impact in the near future.
Then we will apply the concepts we learnt with zero to little coding Involved.
 Machine learning (Supervised and Unsupervised) with IBM Watson
 Natural Language Processing (NLP) with IBM Watson
 Feed Forward Neural Networks (FFNN) with Tensor Flow Simulator
 Convolutional Neural Networks with (CNN) with IBM Watson
 Recurrent Neural Networks (RNN) with Mathworks
Smack in the middle we have easy and intuitive primer sections on how to code using Python, and also how to use popular libraries like Numpy, Pandas, Matplotlib all on the awesome browser based coding platform Jupyter notebook. These middle sections will prepare you for the next sections.
The final set of sections we will take a deeper dive in testing real life use cases and AI applications with Keras, KerasReinforcement, OpenAI Gym and more. The focus will be on building the student's confidence in understanding the data and building solutions. In the final sections you will see a bit more of code but the best part would be that by the end of the sections you will be running AI solutions powered by Deep Neural Networks on a browser with Jupyter Notebook on your Laptop !
 Solving popular data sets like MNIST, CIFAR 10, with CNN, Keras and Jupyter notebook running on your laptop
 Building VGG like nets and Stateful RNN nets using Keras
 Migrating Neural networks from IBM Watson to run on local your Jupyter notebook
 Applying Transfer Learning technique such as Reusing, Retraining with keras
 Testing Reinforcement Learning with Keras and OpenAI Gym
The essence of the later sections will be to understand that there are so many libraries and resources available to you, and that it has been made easy for everyone. You just have to identify what you need to be done and look in the right direction.
AI brings tremendous opportunity like higher economic growth, productivity and prosperity but the picture is not all rosy. lets look at some data points from the renowned Mckinsey&Company.
" 250 million new jobs are likely to be created by 2030"*
" In the midpoint adoption scenario 400 million Jobs are likely to be lost by 2030"*
" In the midpoint adoption scenario 75 million will need change occupational categories by 2030"*
AI is the top priority for Companies, governments and institutions alike. AI surpasses a certain product, or vertical, or function, or a specific industry , it encompasses everything. It is all prevalent.
Based on the report there will be considerable shortages in the IT sector and companies are looking to fill these gaps by retraining, hiring, redeploying, contracting and even hiring from non traditional sources. Technological skill is the TOP skill that will be required during this time and by one research they will need 250,000 data scientists by 2030. If you develop these skills and knowledge , you can take advantage of this revolution irrespective of your role, company or Industry you belong to.
So if you are "AI ready then you are future ready"
AI is here to stay and the ones who get on board fast and adapt to it will be in a much better position to face the exciting but uncertain future.
Choose Success , make yourself invaluable and irreplaceable. I will see "YOU" on the inside.
God Speed.
Reinforcement Learning beginner to master 1
Build Artificial Intelligence (AI) agents using Deep Reinforcement Learning and PyTorch: A2C, REINFORCE, DQN, etc.
Created by Escape Velocity Labs  Handson, comprehensive AI courses
Students: 209, Price: $19.99
Students: 209, Price: Paid
This is the most complete Reinforcement Learning course on Udemy. In it you will learn the basics of Reinforcement Learning, one of the three paradigms of modern artificial intelligence. You will implement from scratch adaptive algorithms that solve control tasks based on experience. You will also learn to combine these algorithms with Deep Learning techniques and neural networks, giving rise to the branch known as Deep Reinforcement Learning.
This course is the first in the "Reinforcement Learning: Beginner to Master" series and will give you the foundation you need to be able to understand new algorithms as they emerge. It will also prepare you for the next courses in this series, in which we will go much deeper into different branches of Reinforcement Learning and look at some of the more advanced algorithms that exist.
The course is focused on developing practical skills. Therefore, after learning the most important concepts of each family of methods, we will implement one or more of their algorithms in jupyter notebooks, from scratch.
This course is divided into three parts and covers the following topics:
Part 1 (Tabular methods):
 Markov decision process
 Dynamic programming
 Monte Carlo methods
 Time difference methods (SARSA, QLearning)
 Nstep bootstrapping
Part 2 (Continuous state spaces):
 State aggregation
 Tile Coding
Part 3 (Deep Reinforcement Learning):
 Deep SARSA
 Deep QLearning
 REINFORCE
 Advantage ActorCritic / A2C (Advantage ActorCritic / A2C method)
Reinforcement Learning: The Complete Course in 2021/2022
Complete guide to Reinforcement Learning, with MAB problems, Games, Taxi problems, and Online Advertising Applications
Created by Hoang Quy La  Electrical Engineer
Students: 139, Price: $49.99
Students: 139, Price: Paid
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 Google’s AlphaGo beat the world Champion in Go.
We saw AIs playing video games like Doom and Super Mario.
Selfdriving cars have started driving on real roads with other drivers and even carrying passengers (Uber), 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.
Learning about supervised and unsupervised machine learning is no small feat.
And yet reinforcement learning opens up a whole new world. As you’ll learn in this course, the reinforcement learning paradigm is more different from supervised and unsupervised learning than they are from each other.
It’s led to new and amazing insights both in behavioural psychology and neuroscience. As you’ll learn in this course, there are many analogous processes when it comes to teaching an agent and teaching an animal or even a human. It’s the closest thing we have so far to a true general artificial intelligence. What’s covered in this course?

Deep Learning.

Google Colab

Anaconda.

Jupiter Notebook.

Activation Function.

Keras.

Pandas.

TensorFlow 2.0

Neural Network

Matplotlib.

scikitlearn.

OpenAI Gym.

Pytorch.

Policy gradient algorithm.

Markov Chain.

Policy iteration algorithm.

Monte Carlo method.

QLearning.

DeepQ networks.

Double DeepQ networks.

Duelling DeepQ networks.

REINFORCE algorithm.

The multiarmed bandit problem.

Ways to calculate means and moving averages and their relationship to stochastic gradient descent.

Markov Decision Processes (MDPs).

Dynamic Programming.

Temporal Difference (TD) Learning (QLearning and SARSA).

Actorcritic algorithm.

Advantage ActorCritic (A2C).

Deep Recurrent QLearning algorithm and DRQN agent Implementation .

Asynchronous Advantage ActorCritic algorithm and A3C agent Implementation.

Proximal Policy Optimization algorithm and PPO agent Implementation .

Deep Deterministic Policy Gradient algorithm and DDPG agent Implementation.

Contextual bandits.
If you’re ready to take on a brand new challenge, and learn about AI techniques that you’ve never seen before in traditional supervised machine learning, unsupervised machine learning, or even deep learning, then this course is for you.
Moreover, the course is packed with practical exercises that are based on reallife examples. So not only will you learn the theory, but you will also get some handson practice building your own models. There are five big projects on healthcare problems and one small project to practice. These projects are listed below:

Robot control.

Hill Climbing game.

Atari game.

Frozen Lake environment.

Coin Flipping gamble

Calculating Pi.

Blackjack game.

Windy Gridworld environment playground.

Taxi problem.

The MAB problem.

Mountain car environment.

Online Advertisement.

Cryptocurrency Trading Agents.

Building Stock/Share Trading Agents.
That is all. 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 course where you will learn how to implement deep REINFORCEMENT 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...