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

Challenges and future directions

You could be wondering why we are back to talking about RL challenges after finishing an advanced-level book on this topic. Indeed, throughout the book, we presented many approaches to mitigate them. On the other hand, we cannot claim these challenges are solved. So, it is important to call them out and discuss the future directions for each in a concise list to give you a mental map and a compass to navigate through them.

Let's start our discussion with one of the most important challenges: Sample efficiency.

Sample efficiency

As you are now well aware, it takes a lot of data to train an RL model. OpenAI Five, who became a world-class player in the strategy game Dota 2, took 128,000 CPUs and 256 CPUs to train, over many months, collecting a total of 900 years' worth of game experience per day (OpenAI, 2018). RL algorithms are benchmarked on their performances after trained over 10 billion Atari frames (Kapturowski, 2019). This is...