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

How to pick the right algorithm?

As in all machine learning domains, there is no silver bullet in terms of which algorithm to use for different applications. There are many criteria you should consider, and in some cases some of them will be more important than others.

Here are different dimensions of algorithm performances that you should look into when picking your algorithm.

  • Highest reward: When you are not bounded by compute and time resources and your goals is simply to train the best possible agent for your application, highest reward is the criterion you should pay attention to. PPO and SAC are promising alternatives here.
  • Sample efficiency: If your sampling process is costly / time-consuming, then sample efficiency (achieving higher rewards using less samples is important). When this is the case, you should look into off-policy algorithms as they reuse past experiences for training as on-policy methods are often incredibly wasteful in how they consume samples...