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

Why we need function approximations

While solving (contextual) multi-armed bandit problems, our goal is to learn action values for each arm (action) from our observations, which we have denoted by . In the online advertising example, it represented our estimate for the probability of a user clicking the ad if we displayed . Now, assume that we have two pieces of information about the user seeing the ad, namely:

  • Device type (e.g. mobile vs. desktop), and
  • Location (e.g. domestic / U.S. vs. international / non-U.S.)

It is quite likely that ad performances will differ with device type and location, which make up the context in this example. A CB model will therefore leverage this information, estimate the action values for each context, and choose the actions accordingly.

This would look like filling a table for each ad similar to the below:

Table 1 – Sample action values for ad D

This means solving four MAB problems, one for...