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30-Day Money-Back Guarantee

This course includes:

  • 6 hours on-demand video
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
Development Data Science R

Reinforcement Learning with R: Algorithms-Agents-Environment

Learn how to utilize algorithms for reward-based learning, as part of Reinforcement Learning with R.
Rating: 3.8 out of 53.8 (7 ratings)
56 students
Created by Packt Publishing
Last updated 3/2019
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Understand and Implement the "Grid World" Problem in R
  • Utilize the Markov Decision Process and Bellman equations
  • Get to know the key terms in Reinforcement Learning
  • Dive into Temporal Difference Learning, an algorithm that combines Monte Carlo methods and dynamic programming
  • Take your Machine Learning skills to the next level with RL techniques
  • Learn R examples of policy evaluation and iteration
  • Implement typical applications for model-based and model-free RL
  • Understand policy evaluation and iteration
  • Master Q-Learning with Greedy Selection Examples in R
  • Master the Simulated Annealing Changed Discount Factor through examples in R

Course content

3 sections • 47 lectures • 6h 14m total length

  • Preview03:57
  • Understanding the RL “Grid World” Problem
    07:06
  • Implementing the Grid World Framework in R
    16:52
  • Navigating Grid World and Calculating Likely Successful Outcomes
    15:34
  • R Example – Finding Optimal Policy Navigating 2 x 2 Grid
    26:10
  • R Example – Updating Optimal Policy Navigating 2 x 2 Grid
    19:06
  • R Example – MDPtoolbox Solution Navigating 2 x 2 Grid
    17:51
  • More MDPtoolbox Function Examples Using R
    10:14
  • R Example – Finding Optimal 3 x 4 Grid World Policy
    10:30
  • R Exercise – Building a 3 x 4 Grid World Environment
    03:27
  • R Exercise Solution – Building a 3 x 4 Grid World Environment
    10:15

  • Preview03:32
  • Install RStudio
    02:40
  • Install Python
    01:47
  • Launch Jupyter Notebook
    03:38
  • Learning Type Distinctions
    02:25
  • Get Started with Reinforcement Learning
    02:42
  • Real-world Reinforcement Learning Examples
    02:13
  • Key Terms in Reinforcement Learning
    04:11
  • OpenAI Gym
    03:53
  • Monte Carlo Method
    05:55
  • Monte Carlo Method in Python
    02:18
  • Monte Carlo Method in R
    03:08
  • Practical Reinforcement Learning in OpenAI Gym
    01:58
  • Markov Decision Process Concepts
    07:44
  • Python MDP Toolbox
    06:41
  • Value and Policy Iteration in Python
    03:32
  • MDP Toolbox in R
    02:49
  • Value Iteration and Policy Iteration in R
    03:10
  • Temporal Difference Learning
    08:23
  • Temporal Difference Learning in Python
    01:53
  • Temporal Difference Learning in R
    02:54

  • Preview05:51
  • R Example – Building Model-Free Environment
    12:05
  • R Example – Finding Model-Free Policy
    11:04
  • R Example – Finding Model-Free Policy (Continued)
    08:27
  • R Example – Validating Model-Free Policy
    10:12
  • Policy Evaluation and Iteration
    12:37
  • R Example – Moving a Pawn with Changed Parameters
    08:35
  • Discount Factor and Policy Improvement
    11:21
  • Monte Carlo Methods
    10:42
  • Environment and Q-Learning Functions with R
    08:37
  • Learning Episode and State-Action Functions in R
    14:27
  • State-Action-Reward-State-Action (SARSA)
    05:44
  • Simulated Annealing – An Alternative to Q-Learning
    10:36
  • Q-Learning with a Discount Factor
    14:35
  • Visual Q-Learning Examples
    11:08

Requirements

  • A basic understanding of Machine Learning concepts is required.

Description

Reinforcement Learning has become one of the hottest research areas in Machine Learning and Artificial Intelligence. You can make an intelligent agent in a few steps: have it semi-randomly explore different choices of movement to actions given different conditions and states, then keep track of the reward or penalty associated with each choice for a given state or action. This Course describes and compares the range of model-based and model-free learning algorithms that constitute Reinforcement Learning algorithms.

This comprehensive 3-in-1 course follows a step-by-step practical approach to getting grips with the basics of Reinforcement Learning with R and build your own intelligent systems. Initially, you’ll learn how to implement Reinforcement Learning techniques using the R programming language. You’ll also learn concepts and key algorithms in Reinforcement Learning. Moving further, you’ll dive into Temporal Difference Learning, an algorithm that combines Monte Carlo methods and dynamic programming. Finally, you’ll implement typical applications for model-based and model-free RL.

Towards the end of this course, you'll get to grips with the basics of Reinforcement Learning with R and build your own intelligent systems.

Contents and Overview

This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Reinforcement Learning Techniques with R, covers Reinforcement Learning techniques with R. This Course will give you a brief introduction to Reinforcement Learning; it will help you navigate the "Grid world" to calculate likely successful outcomes using the popular MDPToolbox package. This video will show you how the Stimulus - Action - Reward algorithm works in Reinforcement Learning. By the end of this Course, you will have a basic understanding of the concept of reinforcement learning, you will have compiled your first Reinforcement Learning program, and will have mastered programming the environment for Reinforcement Learning.

The second course, Practical Reinforcement Learning - Agents and Environments, covers concepts and Key Algorithms in Reinforcement Learning. In this course, you’ll learn how to code the core algorithms in RL and get to know the algorithms in both R and Python. This video course will help you hit the ground running, with R and Python code for Value Iteration, Policy Gradients, Q-Learning, Temporal Difference Learning, the Markov Decision Process, and Bellman Equations, which provides a framework for modelling decision making where outcomes are partly random and partly under the control of a decision maker. At the end of the video course, you’ll know the main concepts and key algorithms in RL.

The third course, Discover Algorithms for Reward-Based Learning in R, covers Model-Based and Model-Free RL Algorithms with R. The Course starts by describing the differences in model-free and model-based approaches to Reinforcement Learning. It discusses the characteristics, advantages and disadvantages, and typical examples of model-free and model-based approaches. We look at model-based approaches to Reinforcement Learning. We discuss State-value and State-action value functions, Model-based iterative policy evaluation, and improvement, MDP R examples of moving a pawn, how the discount factor, gamma, “works” and an R example illustrating how the discount factor and relative rewards affect policy. Next, we learn the model-free approach to Reinforcement Learning. This includes Monte Carlo approach, Q-Learning approach, More Q-Learning explanation and R examples of varying the learning rate and randomness of actions and SARSA approach. Finally, we round things up by taking a look at model-free Simulated Annealing and more Q-Learning algorithms. The primary aim is to learn how to create efficient, goal-oriented business policies, and how to evaluate and optimize those policies, primarily using the MDP toolbox package in R. Finally, the video shows how to build actions, rewards, and punishments with a simulated annealing approach.

Towards the end of this course, you'll get to grips with the basics of Reinforcement Learning with R and build your own intelligent systems.

About the Authors

  • Dr. Geoffrey Hubona held a full-time tenure-track, and tenured, assistant, and associate professor faculty positions at three major state universities in the Eastern United States from 1993-2010. In these positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, masters and Ph.D. students. Dr. Hubona earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL (1993); an MA in Economics (1990), also from USF; an MBA in Finance (1979) from George Mason University in Fairfax, VA; and a BA in Psychology (1972) from the University of Virginia in Charlottesville, VA.

  • Lauren Washington is currently the Lead Data Scientist and Machine Learning Developer for smartQED , an AI-driven start-up. Lauren worked as a Data Scientist for Topix, Payments Risk Strategist for Google (Google Wallet/Android Pay), Statistical Analyst for Nielsen, and Big Data Intern for the National Opinion Research Center through the University of Chicago. Lauren is also passionate about teaching Machine Learning. She’s currently giving back to the data science community as a Thankful Data Science Bootcamp Mentor and a Packt Publishing technical video reviewer. She also earned a Data Science certificate from General Assembly San Francisco (2016), an MA in the Quantitative Methods in the Social Sciences (Applied Statistical Methods) from Columbia University (2012), and a BA in Economics from Spelman College (2010). Lauren is a leader in AI, in Silicon Valley, with a passion for knowledge gathering and sharing.

Who this course is for:

  • Data Scientists and AI programmers who are new to reinforcement learning and want to learn the fundamentals of building self-learning intelligent agents in a practical way.

Instructor

Packt Publishing
Tech Knowledge in Motion
Packt Publishing
  • 3.9 Instructor Rating
  • 56,509 Reviews
  • 344,368 Students
  • 1,423 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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