Book Image

The Reinforcement Learning Workshop

By : Alessandro Palmas, Emanuele Ghelfi, Dr. Alexandra Galina Petre, Mayur Kulkarni, Anand N.S., Quan Nguyen, Aritra Sen, Anthony So, Saikat Basak
Book Image

The Reinforcement Learning Workshop

By: Alessandro Palmas, Emanuele Ghelfi, Dr. Alexandra Galina Petre, Mayur Kulkarni, Anand N.S., Quan Nguyen, Aritra Sen, Anthony So, Saikat Basak

Overview of this book

Various intelligent applications such as video games, inventory management software, warehouse robots, and translation tools use reinforcement learning (RL) to make decisions and perform actions that maximize the probability of the desired outcome. This book will help you to get to grips with the techniques and the algorithms for implementing RL in your machine learning models. Starting with an introduction to RL, youÔÇÖll be guided through different RL environments and frameworks. YouÔÇÖll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once youÔÇÖve explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, youÔÇÖll understand when to apply the different deep learning methods in RL and advance to deep Q-learning. The book will even help you understand the different stages of machine-based problem-solving by using DARQN on a popular video game Breakout. Finally, youÔÇÖll find out when to use a policy-based method to tackle an RL problem. By the end of The Reinforcement Learning Workshop, youÔÇÖll be equipped with the knowledge and skills needed to solve challenging problems using reinforcement learning.
Table of Contents (14 chapters)
Preface
Free Chapter
2
2. Markov Decision Processes and Bellman Equations

Importance Sampling

Monte Carlo methods can be on-policy or off-policy. In on-policy learning, we learn from the agent experience of the following policy. In off-policy learning, we learn how to estimate a target policy from the experience of following a different behavioral policy. Importance sampling is a key technique for off-policy learning. The following figure compares on-policy and off-policy learning:

Figure 6.7: On-Policy versus Off-Policy comparison

You might think that on-policy learning is learning while playing, while off-policy learning is learning by watching someone else play. You could improve your cricket game by playing cricket yourself. This will help you learn from your mistakes and best actions. That would be on-policy learning. You could also learn by watching others play the game of cricket and learning from their mistakes and best actions. That would be off-policy learning.

Human beings typically do both on-policy and off-policy...