Reinforcement Learning with R- Algorithms-Agents-Environment

Reinforcement Learning with R: Algorithms-Agents-Environment, available at $44.99, has an average rating of 4.75, with 47 lectures, based on 26 reviews, and has 125 subscribers.

You will learn about 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 This course is ideal for individuals who are 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. It is particularly useful 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.

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Summary

Title: Reinforcement Learning with R: Algorithms-Agents-Environment

Price: $44.99

Average Rating: 4.75

Number of Lectures: 47

Number of Published Lectures: 47

Number of Curriculum Items: 47

Number of Published Curriculum Objects: 47

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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.

Target Audiences

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.

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 Hubonaheld 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 Washingtonis 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.

Course Curriculum

Chapter 1: Reinforcement Learning Techniques with R

Lecture 1: The Course Overview

Lecture 2: Understanding the RL “Grid World” Problem

Lecture 3: Implementing the Grid World Framework in R

Lecture 4: Navigating Grid World and Calculating Likely Successful Outcomes

Lecture 5: R Example – Finding Optimal Policy Navigating 2 x 2 Grid

Lecture 6: R Example – Updating Optimal Policy Navigating 2 x 2 Grid

Lecture 7: R Example – MDPtoolbox Solution Navigating 2 x 2 Grid

Lecture 8: More MDPtoolbox Function Examples Using R

Lecture 9: R Example – Finding Optimal 3 x 4 Grid World Policy

Lecture 10: R Exercise – Building a 3 x 4 Grid World Environment

Lecture 11: R Exercise Solution – Building a 3 x 4 Grid World Environment

Chapter 2: Practical Reinforcement Learning – Agents and Environments

Lecture 1: The Course Overview

Lecture 2: Install RStudio

Lecture 3: Install Python

Lecture 4: Launch Jupyter Notebook

Lecture 5: Learning Type Distinctions

Lecture 6: Get Started with Reinforcement Learning

Lecture 7: Real-world Reinforcement Learning Examples

Lecture 8: Key Terms in Reinforcement Learning

Lecture 9: OpenAI Gym

Lecture 10: Monte Carlo Method

Lecture 11: Monte Carlo Method in Python

Lecture 12: Monte Carlo Method in R

Lecture 13: Practical Reinforcement Learning in OpenAI Gym

Lecture 14: Markov Decision Process Concepts

Lecture 15: Python MDP Toolbox

Lecture 16: Value and Policy Iteration in Python

Lecture 17: MDP Toolbox in R

Lecture 18: Value Iteration and Policy Iteration in R

Lecture 19: Temporal Difference Learning

Lecture 20: Temporal Difference Learning in Python

Lecture 21: Temporal Difference Learning in R

Chapter 3: Discover Algorithms for Reward-Based Learning in R

Lecture 1: The Course Overview

Lecture 2: R Example – Building Model-Free Environment

Lecture 3: R Example – Finding Model-Free Policy

Lecture 4: R Example – Finding Model-Free Policy (Continued)

Lecture 5: R Example – Validating Model-Free Policy

Lecture 6: Policy Evaluation and Iteration

Lecture 7: R Example – Moving a Pawn with Changed Parameters

Lecture 8: Discount Factor and Policy Improvement

Lecture 9: Monte Carlo Methods

Lecture 10: Environment and Q-Learning Functions with R

Lecture 11: Learning Episode and State-Action Functions in R

Lecture 12: State-Action-Reward-State-Action (SARSA)

Lecture 13: Simulated Annealing – An Alternative to Q-Learning

Lecture 14: Q-Learning with a Discount Factor

Lecture 15: Visual Q-Learning Examples

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 2 votes

2 stars: 1 votes

3 stars: 1 votes

4 stars: 11 votes

5 stars: 11 votes

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