Last updated 3/2019
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.83 GB | Duration: 6h 13m
Learn how to utilize algorithms for reward-based learning, as part of Reinforcement Learning with R.
Learn how to utilize algorithms for reward-based learning, as part of Reinforcement Learning with R.
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
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 OverviewThis 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 AuthorsDr. 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.
Overview
Section 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
Section 2: Practical Reinforcement Learning – Agents and Environments
Lecture 12 The Course Overview
Lecture 13 Install RStudio
Lecture 14 Install Python
Lecture 15 Launch Jupyter Notebook
Lecture 16 Learning Type Distinctions
Lecture 17 Get Started with Reinforcement Learning
Lecture 18 Real-world Reinforcement Learning Examples
Lecture 19 Key Terms in Reinforcement Learning
Lecture 20 OpenAI Gym
Lecture 21 Monte Carlo Method
Lecture 22 Monte Carlo Method in Python
Lecture 23 Monte Carlo Method in R
Lecture 24 Practical Reinforcement Learning in OpenAI Gym
Lecture 25 Markov Decision Process Concepts
Lecture 26 Python MDP Toolbox
Lecture 27 Value and Policy Iteration in Python
Lecture 28 MDP Toolbox in R
Lecture 29 Value Iteration and Policy Iteration in R
Lecture 30 Temporal Difference Learning
Lecture 31 Temporal Difference Learning in Python
Lecture 32 Temporal Difference Learning in R
Section 3: Discover Algorithms for Reward-Based Learning in R
Lecture 33 The Course Overview
Lecture 34 R Example – Building Model-Free Environment
Lecture 35 R Example – Finding Model-Free Policy
Lecture 36 R Example – Finding Model-Free Policy (Continued)
Lecture 37 R Example – Validating Model-Free Policy
Lecture 38 Policy Evaluation and Iteration
Lecture 39 R Example – Moving a Pawn with Changed Parameters
Lecture 40 Discount Factor and Policy Improvement
Lecture 41 Monte Carlo Methods
Lecture 42 Environment and Q-Learning Functions with R
Lecture 43 Learning Episode and State-Action Functions in R
Lecture 44 State-Action-Reward-State-Action (SARSA)
Lecture 45 Simulated Annealing – An Alternative to Q-Learning
Lecture 46 Q-Learning with a Discount Factor
Lecture 47 Visual Q-Learning Examples
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.
https://www.udemy.com/course/reinforcement-learning-with-r-algorithms-agents-environment/
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