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

Planning through a model

In this section, we first define what it means to plan through a model in the sense of optimal control. Then, we will cover several planning methods, including the cross-entropy method and covariance matrix adaptation evolution strategy. You will also see how these methods can be parallelized using the Ray library. Now, let's get started with the problem definition.

Defining the optimal control problem

In RL, or in control problems in general, we care about the actions an agent takes because there is a task that we want to be achieved. We express this task as a mathematical objective so that we can use mathematical tools to figure out the actions toward the task – and in RL, this is the expected sum of cumulative discounted rewards. You of course know all this, as this is what we have been doing all along, but this is a good time to reiterate it: We are essentially solving an optimization problem here.

Now, let's assume that we are...