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

Getting familiar with the Kuka environment

KUKA is a company that offers industrial robotics solutions, which are widely used in manufacturing and assembly environments. PyBullet includes a simulation of a KUKA robot, used for object grasping simulations (Figure 14.3).

Figure 14.3 – KUKA robots are widely used in industry. (a) A real KUKA robot (image source CNC Robotics website), (b) a PyBullet simulation

Figure 14.3 – KUKA robots are widely used in industry. (a) A real KUKA robot (image source CNC Robotics website), (b) a PyBullet simulation.

There are multiple Kuka environments in PyBullet for:

  • Grasping a rectangle block using robot and object position and angles,
  • Grasping a rectangle block using camera inputs,
  • Grasping random objects using camera/position inputs.

In this chapter, we focus on the first one, which we look into next in more detail.

Grasping a rectangle block using a Kuka robot

In this environment, the goal of the robot is to reach a rectangle object, grasp it, and raise it up to a certain height. An example scene from the environment, along...