Book Image

Mastering Reinforcement Learning with Python

By : Enes Bilgin
Book Image

Mastering Reinforcement Learning with Python

By: Enes Bilgin

Overview of this book

Reinforcement learning (RL) is a field of artificial intelligence (AI) used for creating self-learning autonomous agents. Building on a strong theoretical foundation, this book takes a practical approach and uses examples inspired by real-world industry problems to teach you about state-of-the-art RL. Starting with bandit problems, Markov decision processes, and dynamic programming, the book provides an in-depth review of the classical RL techniques, such as Monte Carlo methods and temporal-difference learning. After that, you will learn about deep Q-learning, policy gradient algorithms, actor-critic methods, model-based methods, and multi-agent reinforcement learning. Then, you'll be introduced to some of the key approaches behind the most successful RL implementations, such as domain randomization and curiosity-driven learning. As you advance, you’ll explore many novel algorithms with advanced implementations using modern Python libraries such as TensorFlow and Ray’s RLlib package. You’ll also find out how to implement RL in areas such as robotics, supply chain management, marketing, finance, smart cities, and cybersecurity while assessing the trade-offs between different approaches and avoiding common pitfalls. By the end of this book, you’ll have mastered how to train and deploy your own RL agents for solving RL problems.
Table of Contents (24 chapters)
Section 1: Reinforcement Learning Foundations
Section 2: Deep Reinforcement Learning
Section 3: Advanced Topics in RL
Section 4: Applications of RL


In this chapter, we have covered an emerging paradigm in artificial intelligence, machine teaching, which is about effectively conveying the expertise of a subject matter expert (teacher) to machine learning model training. We discussed how this is similar to how humans are educated: By building on others' knowledge. The advantage of this approach is that it greatly increases data efficiency in machine learning, and, in some cases, makes learning possible that would have been impossible without a teacher. We discussed various methods in this paradigm, including reward function engineering, curriculum learning, demonstration learning, action masking, and concept networks. We observed how some of these methods improved vanilla use of Ape-X DQN significantly.

Besides its benefits, machine teaching also has some challenges and potential downsides: First, it is usually non-trivial to come up with good reward shaping, curriculum, set of action masking conditions etc. This...