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Development Data Science Reinforcement Learning

Hands - On Reinforcement Learning with Python

A practical tour of prediction and control in Reinforcement Learning using OpenAI Gym, Python, and TensorFlow
Rating: 3.7 out of 53.7 (60 ratings)
318 students
Created by Packt Publishing
Last updated 4/2018
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • After completing the course, you can delve into specific RL topics, and start solving more complex RL problems on OpenAI Gym or similar.

Course content

8 sections • 32 lectures • 4h 27m total length

  • Preview03:47
  • Understanding Reinforcement Learning Algorithms
    08:53
  • Installing and Setting Up OpenAI Gym
    03:07
  • Running a Visualization of the Cart Robot CartPole-v0 in OpenAI Gym
    07:28

  • Preview11:46
  • Understanding the Environment of CartPole in OpenAI Gym
    03:27
  • Coding up Your First Solution to CartPole-v0
    15:42

  • Creating a Bandit with 4 Arms Using Python and Numpy
    09:11
  • Creating an Agent to Solve the MAB Problem Using Python and Tensorflow
    10:10
  • Training the Agent, and Understanding What It Learned
    06:22

  • Creating an Environment with Multiple Bandits Using Python and Numpy
    10:24
  • Creating Your First Policy Gradient Based RL Agent with TensorFlow
    08:41
  • Training the Agent, and Understanding What It Learned
    09:13

  • Visualizing Dynamic Programming in GridWorld in Your Browser
    11:39
  • Understanding Prediction Through Building a Policy Evaluation Algorithm
    11:07
  • Understanding Control Through Building a Policy Iteration Algorithm
    11:06
  • Building a Value Iteration Algorithm
    09:45
  • Linking It All Together in the Web-Based GridWorld Visualization
    05:49

  • Understanding Markov Decision Process and Dynamic Programming in CartPole-v0
    06:59
  • Crafting a Neural Network Using TensorFlow
    09:33
  • Crafting a Neural Network to Predict the Value of Being in Different Environment
    08:21
  • Training the Agent in CartPole-v0
    11:44
  • Visualizing and Understanding How Your Software Agent Has Performed
    06:13

  • Running the Blackjack Environment From the OpenAI Gym
    04:35
  • Tallying Every Outcome of an Agent Playing Blackjack Using MC
    08:57
  • Visualizing the Outcomes of a Simple Blackjack Strategy
    08:21
  • Control – Building a Very Simple Epsilon-Greedy Policy
    08:17
  • Visualizing the Outcomes of the Epsilon-Greedy Policy
    04:59

  • Visualizing TD and SARSA in GridWorld in Your Browser
    08:13
  • Running the GridWorld Environment from the OpenAI Gym
    08:16
  • Building a SARSA Algorithm to Find the Optimal Epsilon-Greedy Policy
    08:08
  • Visualizing the Outcomes of the SARSA
    07:38

Requirements

  • No prior Reinforcement Learning knowledge is required, although knowing Python and having a quantitative background will help you follow the video more effectively.

Description

Reinforcement learning (RL) is hot! This branch of machine learning powers AlphaGo and Deepmind's Atari AI. It allows programmers to create software agents that learn to take optimal actions to maximize reward, through trying out different strategies in a given environment.

This course will take you through all the core concepts in Reinforcement Learning, transforming a theoretical subject into tangible Python coding exercises with the help of OpenAI Gym. The videos will first guide you through the gym environment, solving the CartPole-v0 toy robotics problem, before moving on to coding up and solving a multi-armed bandit problem in Python. As the course ramps up, it shows you how to use dynamic programming and TensorFlow-based neural networks to solve GridWorld, another OpenAI Gym challenge. Lastly, we take the Blackjack challenge and deploy model free algorithms that leverage Monte Carlo methods and Temporal Difference (TD, more specifically SARSA) techniques.

The scope of Reinforcement Learning applications outside toy examples is immense. Reinforcement Learning can optimize agricultural yield in IoT powered greenhouses, and reduce power consumption in data centers. It's grown in demand to the point where its applications range from controlling robots to extracting insights from images and natural language data. By the end of this course, you will not only be able to solve these problems but will also be able to use Reinforcement Learning as a problem-solving strategy and use different algorithms to solve these problems.

This course uses Python 3.6, while not the latest version available, it provides relevant and informative content for legacy users of Python.

About the Author

Colibri is a technology consultancy company founded in 2015 by James Cross and Ingrid Funie. The company works to help its clients navigate the rapidly changing and complex world of emerging technologies, with deep expertise in areas such as big data, data science, machine learning, and cloud computing. Over the past few years they have worked with some of the world's largest and most prestigious companies, including a tier-1 investment bank, a leading management consultancy group, and one of the world's most popular soft drinks companies, helping each of them to better make sense of its data, and process it in more intelligent ways. The company lives by its motto: Data -> Intelligence -> Action.

Rudy Lai is the founder of QuantCopy, a sales acceleration startup using AI to write sales emails to prospects. By taking in leads from your pipelines, QuantCopy researches them online and generates sales emails from that data. It also has a suite of email automation tools to schedule, send, and track email performance - key analytics that all feedback into how our AI generates content.

Prior to founding QuantCopy, Rudy ran HighDimension.IO, a machine learning consultancy, where he experienced first hand the frustrations of outbound sales and prospecting. As a founding partner, he helped startups and enterprises with HighDimension.IO’s Machine-Learning-as-a-Service, allowing them to scale up data expertise in the blink of an eye. In the first part of his career, Rudy spent 5+ years in quantitative trading at leading investment banks such as Morgan Stanley. This valuable experience allowed him to witness the power of data, but also the pitfalls of automation using data science and machine learning. Quantitative trading was also a great platform to learn deeply about reinforcement learning and supervised learning topics in a commercial setting.

Rudy holds a Computer Science degree from Imperial College London, where he was part of the Dean’s List, and received awards such as the Deutsche Bank Artificial Intelligence prize.

Who this course is for:

  • This course is intended for people who have some understanding of supervised learning, and are interested in artificial intelligence.

Instructor

Packt Publishing
Tech Knowledge in Motion
Packt Publishing
  • 3.9 Instructor Rating
  • 58,056 Reviews
  • 349,946 Students
  • 1,418 Courses

Packt has been committed to developer learning since 2004. A lot has changed in software since then - but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.

With an extensive library of content - more than 4000 books and video courses -Packt's mission is to help developers stay relevant in a rapidly changing world. From new web frameworks and programming languages, to cutting edge data analytics, and DevOps, Packt takes software professionals in every field to what's important to them now.

From skills that will help you to develop and future proof your career to immediate solutions to every day tech challenges, Packt is a go-to resource to make you a better, smarter developer.

Packt Udemy courses continue this tradition, bringing you comprehensive yet concise video courses straight from the experts.



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