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

Using curriculum learning to train the Kuka robot

The first step before actually kicking off some training is to customize the Kuka class as well as the KukaGymEnv to make them work with the curriculum learning parameters we described above. So, let's do that next.

Customizing the environment for curriculum learning

First, we start with creating a CustomKuka class which inherits the original Kuka class of PyBullet. Here is how we do it:


  1. We first need to create the new class, and accept an additional argument, jp_override dictionary, which stands for joint position override.
    class CustomKuka(Kuka):
        def __init__(self, *args, 
                     jp_override=None, **kwargs):
            self.jp_override = jp_override
            super(CustomKuka, self).__init__(*args...