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

Chapter 14: Solving Robot Learning

So far in the book, we have covered many state-of-the-art algorithms and approaches in reinforcement learning. Now, starting with this chapter, we will see them in action to take on real-world problems! We start with robot learning, an important application area for reinforcement learning. To this end, we will train a Kuka robot to grasp objects on a tray using PyBullet physics simulation. We will discuss several ways of solving this hard-exploration problem and solve it both using a manually crafted curriculum as well as using the ALP-GMM algorithm. At the end of the chapter, we will present other simulation libraries for robotics and autonomous driving, which are commonly used to train reinforcement learning agents.

So, this chapter covers:

  • Introducing PyBullet
  • Getting familiar with the Kuka environment
  • Developing strategies to solve the Kuka environment
  • Using curriculum learning to train the Kuka robot
  • Going beyond PyBullet...