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)
1
Section 1: Reinforcement Learning Foundations
7
Section 2: Deep Reinforcement Learning
12
Section 3: Advanced Topics in RL
17
Section 4: Applications of RL

Introducing PyBullet

PyBullet is a popular high-fidelity physics simulation for robotics, machine learning, games, and more. It is one of the most commonly used libraries for robot learning using RL, especially in sim-to-real transfer research and applications.

Figure 14.1 – PyBullet environments and visualizations (source: PyBullet GitHub repo)

PyBullet allows developers to create their own physics simulations. In addition, it has prebuilt environments using the OpenAI Gym interface. Some of those environments are shown in Figure 14.1.

In the next section, we will set up a virtual environment for PyBullet.

Setting up PyBullet

It is almost always a good idea to work in virtual environments for Python projects, which is also what we do for our robot learning experiments in this chapter. So, let's go ahead and execute the following commands to install the libraries we will use:

$ virtualenv pybenv
$ source pybenv/bin/activate
$ pip install...