Course details
Users will be taken through a journey that starts by showing them the various algorithms that can be used for reward-based learning. The video describes and compares the range of model-based and model-free learning algorithms that constitute RL algorithms.
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-… + Read More
Course details
Users will be taken through a journey that starts by showing them the various algorithms that can be used for reward-based learning. The video describes and compares the range of model-based and model-free learning algorithms that constitute RL algorithms.
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 MDPtoolbox package in R. Finally, the video shows how to build actions, rewards, and punishments with a simulated annealing approach.
About the Author :
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, master's 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.
Updated on 02 January, 2018 - Read Less
