# Reinforcement Learning with R: Algorithms-Agents-Environment

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Try Udemy for Business- 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 video provides an overview of the entire course.

The aim of this video is to introduce Reinforcement Learning (RL) and illustrate RL concepts with a prototypical example.

Contrast RL with supervised and unsupervised learning

Introduce the classic RL Grid World problem or framework

Explain the RL concepts of states and actions, covering important RL concepts

The aim of this video is to demonstrate how to represent Grid World using the R software and to introduce the RL concepts of sequences of actions and randomness of actions.

Show how to represent (code) Grid World in R

Explain in detail the importance of the sequences of actions in achieving rewards

Show how to represent stochasticity or possible randomness in action behavior, covering all of the aim intentions

The aim is twofold: one isto probe more deeply into how the possible random execution of actions can affect the outcome, andthe second is to demonstrate that the specific reward structure can affect the optimal policy with regard to the best action.

Demonstrate how stochasticity affects ultimate action outcome

Examine the optimal policy in Grid World, given the different reward structures.

Show that small changes in reward structure matter!

The video deals with developing the optimal policy as a model-free solution to navigating a 2 x 2 grid.

Describe two different R packages for solving RL problems

Show RL state-action-reward framework

Demonstrate a hands-on extended R example to find an optimal policy

This video addresses the epsilon-greedy action selection strategy to update the optimal policy with a model-free solution to navigating a 2 x 2 grid.

Describe distinctions between exploration and exploitation action selection approaches

Describe the implementation of epsilon-greedy action selection strategy

Use another hands-on extended R example to update, or validate an optimal policy using an existing model

This video deals with using the R MDPtoolbox package to find the optimal policy solution for navigating a 2 x 2 grid.

Describe the Markov Decision Process framework for a Reinforcement Learning problem

Detail the probabilistic nature of the transition model

Demonstrate an MDPtoolbox R example to find the optimal policy

This video identifies and demonstrates several of the more important MDPtoolbox functions as pertinent to Reinforcement Learning problems.

Introduces several of the more important MDPtoolbox functions

Demonstrates what these MDPtoolbox functions do

Shows the input and output from each respective MDPtoolbox function

This video closes the loop on representing the 3 x 4 Grid World RL problem using R and without using any RL-specific R packages.

Show how to solve the original 3 x 4 Grid RL problem

Show how to construct a representative 3 x 4 Grid World environment

Demonstrate that this manualsolution produces a similar optimal policy

This video presents an end-of-Title user exercise, integrating much of the material presented in the three sections.

Frame a user exercise to reinforce learning this Title’s material

Provide the stub code to complete the user exercise

Challenge the user to build an appropriate environment in R

This video present a solution to the end-of-Title user exercise presented in the preceding video.

Detail the steps needed to solve the user exercise

Show how to build the appropriate R objects to complete the exercise

Demonstrate that the user solution produces the same optimal policy as before

This video will give you an overview about the course.

This video provides an overview of the entire course.

How do you represent the environment when you have no explicit MDP model?

Determine the rules, “

**Physics**,” structure of the state spaceDetermine the possible states, actions, new states, and rewards, and what you need to do once you have determined all of that

Build an environment function in R

How does one validate the model, as well as validate (and possibly update) the previously-determined optimal policy?

Sample a new set of data from environment

Determine optimal policy function again, with the same model

Then compare the new policy function with the previous policy function

How gamma affects policy improvement and optimal policy determination by diving deeper into the nature of the discount factor, gamma?

Explain how the discount factor determines the value function

Show how the value function determines policy

Present an R example of discount and rewards affecting policy

What is the nature of the Simulated Annealing algorithm alternative to Q-Learning?

Describe the characteristics of the Simulated Annealing approach

Describe probabilistic action selection derived from Boltzmann distribution metaheuristic

Illustrate with an R simulated annealing 2x2 grid example

How does one incorporate the discount factor into the previous Model-Free Q-Learning Reinforcement Learning algorithm?

Modify the Q-Learning algorithm to include a discount factor

Include the aggregation of rewards by episode

Illustrate modified Q-Learning algorithm with R examples

How does one demonstrate the effects of Q-Learning algorithm control parameters using effective visualizations?

Use the popular R ggplot2 package to create visualizations

Examine effects of epsilon, alpha, and gamma control parameters

Create color-based line plots of Q-values and rewards

- A basic understanding of Machine Learning concepts is required.

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 c**ompares 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.

- 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.