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

Training your agent with Monte Carlo methods

Let's say you would like to learn the chance of flipping heads with a particular, possibly biased, coin:

  • One way of calculating this is through a careful analysis of the physical properties of the coin. Although this could give you the precise probability distribution of the outcomes, it is far from being a practical approach.
  • Alternatively, you can just flip the coin many times and look at the distribution in your sample. Your estimate could be a bit off if you don't have a large sample, but it will do the job for most practical purposes. The math you need to deal with using the latter method will be incomparably simpler.

Just like in the coin example, we can estimate the state values and action values in an MDP from random samples. Monte Carlo (MC) estimation is a general concept that refers to making estimations through repeated random sampling. In the context of RL, it refers to a collection of methods...