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

Comparison of the policy-based methods in Lunar Lander

Below is a comparison of evaluation reward performance progress for different policy-based algorithms over a single training session in the Lunar Lander environment:

Figure 7.6 – Lunar Lander training performance of various policy-based algorithms

To also give a sense of how long each training session took and what was the performance at the end of the training, below is TensorBoard tooltip for the plot above:

Figure 7.7 – Wall-clock time and end-of-training performance comparisons

Before going into further discussions, let's make the following disclaimer: The comparisons here should not be taken as a benchmark of different algorithms for multiple reasons:

  • We did not perform any hyper-parameter tuning,
  • The plots come from a single training trial for each algorithm. Training an RL agent is a highly stochastic process and a fair comparison should include...