Start with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and policy-based methods with this practical guide
Key Features
- Use TensorFlow to write reinforcement learning agents for performing challenging tasks
- Learn how to solve finite Markov decision problems
- Train models to understand popular video games like Breakout
Book Description
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, you’ll be guided through different RL environments and frameworks. You’ll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once you’ve explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, you’ll 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, you’ll find out when to use a policy-based method to tackle an RL problem.
By the end of The Reinforcement Learning Workshop, you’ll be equipped with the knowledge and skills needed to solve challenging problems using reinforcement learning.
What you will learn
- Use OpenAI Gym as a framework to implement RL environments
- Find out how to define and implement reward function
- Explore Markov chain, Markov decision process, and the Bellman equation
- Distinguish between Dynamic Programming, Monte Carlo, and Temporal Difference Learning
- Understand the multi-armed bandit problem and explore various strategies to solve it
- Build a deep Q model network for playing the video game Breakout
Who this book is for
If you are a data scientist, machine learning enthusiast, or a Python developer who wants to learn basic to advanced deep reinforcement learning algorithms, this workshop is for you. A basic understanding of the Python language is necessary.
Alessandro Palmas is an aerospace engineer with more than 7 years of proven expertise in software development for advanced scientific applications and complex software systems. As the R&D head in an aerospace & defense Italian SME, he coordinates projects in contexts ranging from space flight dynamics to machine learning-based autonomous systems. His main ML focus is on computer vision, 3D models, volumetric networks, and deep reinforcement learning. He also founded innovative initiatives, his last being Artificial Twin, which provides advanced technologies for machine learning, physical modeling, and computational geometry applications. Two key areas in which current Artificial Twin deep RL work is focused on are video games entertainment, and guidance, navigation & control systems.
Emanuele Ghelfi is a computer science and machine learning engineer. He received an M.Sc. degree in computer science and engineering at Politecnico di Milano in December 2018. In his thesis, he proposed a new RL algorithm for an MDP extension. The paper from the thesis got accepted at ICML 2019. He's an organizer of the community data science and artificial intelligence in Parma. Emanuele presented tutorials about generative adversarial networks at conferences like PyCon X (Florence) and EuroSciPy (Bilbao). He is also a developer of the machine learning package AshPy, available on GitHub and PyPi.
Dr. Alexandra Galina Petre is a machine learning and data science expert, currently leading and teaching various engineering modules in Coventry, United Kingdom. Her leadership and management experience is linked to her work in quality management for the Airbus A380 and her IET membership. She received her Ph.D. in user feedback-based reinforcement learning for vehicle comfort control with a focus on revolutionary heating ventilation and air conditioning SARSA-based control systems that can learn from the driver's preferential changes to the UI. Her research is focusing on how thermal comfort depends on the occupant's inclination to manual control as outlined in the SAE paper published in 2019, and the development of a novel Java-based user model (UBL) integrated within a car cabin environment. She is working on deep RL implementations in Python and R-based statistical developments within various automation and control projects.
