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

Engineering the reward function

Reward function engineering means crafting the reward dynamics of the environment in an RL problem so that it reflects the objective you have in your mind for your agent and leads the agent to that objective. How you define your reward function might make the training easy, difficult, or even impossible for the agent. Therefore, in most RL projects, a significant effort is dedicated to designing the reward. In this section, we cover some specific cases where you will need to do it and how, then provide a specific example, and finally discuss the challenges that come with engineering the reward function.

When to engineer the reward function

Multiple times in the book, including the previous section when we discussed concepts, we mentioned how sparse rewards pose a problem for learning. One way of dealing with this is to shape the reward to make it non-sparse. Sparse reward case, therefore, is a common reason of why we may want to do reward function...