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

Quantifying generalization via CoinRun

There are various ways of testing whether certain algorithms/approaches generalize to unseen environment conditions better than others, such as:

  • Creating validation and test environments with separate sets of environment parameters,
  • Assessing policy performance in real-life deployment.

Real-life deployment may not necessarily be an option, so the latter is not always practical. The challenge with the former is to have consistency and to ensure that validation/test data are indeed not used in training. Also, it is possible to overfit to the validation environment when too many models are tried based on validation performance. One approach to overcome these challenges is to use procedurally generated environments. To this end, OpenAI has created the CoinRun environment to benchmark algorithms on their generalization capabilities. Let's look into it in more detail.

CoinRun environment

In the CoinRun environment, we have...