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

Using function approximation for context

Function approximations allow us to model the dynamics of a process from which we have observed data, such as contexts and ad clicks. As in the previous chapter, consider an online advertising scenario with five different ads (i.e. A, B, C, D, and E), with the context comprised of user device, location and age. In this section, our agent will learn five different Q functions, one per ad, each receiving a context , and return the action value estimate. This is illustrated in Figure 1.

Figure 3.1 – We learn a function for each action that receives the context and returns the action value.

At this point, we have a supervised machine learning problem to solve for each action. We can use different models to obtain the Q functions, such as logistic regression or a neural network (which actually allows us to use a single network that estimates values for all actions). Once we choose the type of function approximation...