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

Chapter 11: Achieving Generalization and Overcoming Partial Observability

Deep reinforcement learning (RL) has achieved what was impossible with the earlier AI methods, such as beating world champions in games like Go, Dota 2, and StarCraft II. Yet, applying RL to real-world problems is still challenging. Two important obstacles to this end are generalization of trained policies to a broad set of environment conditions and developing policies that can handle partial observability. As we will see in the chapter, these are closely related challenges, for which we will present solution approaches.

Here is what we will cover in this chapter:

  • Focusing on generalization in reinforcement learning
  • Enriching agent experience via domain randomization
  • Using memory to overcome partial observability
  • Quantifying generalization via CoinRun

These topics are critical to understand for a successful implementation of RL in real-world settings. So, let's dive right in...