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 8: Model-Based Methods

All of the deep reinforcement learning (RL) algorithms we have covered so far were model-free, which means they did not assume any knowledge about the transition dynamics of the environment but learned from sampled experiences. In fact, this was a quite deliberate departure from the dynamic programming methods to save us from requiring a model of the environment. In this chapter, we swing the pendulum back a little bit and discuss a class of methods that rely on a model, called model-based methods. These methods can lead to improved sample efficiency by several orders of magnitude in some problems, making it a very appealing approach, especially when collecting experience is as costly as in robotics. Having said this, we still will not assume that we have such a model readily available, but we will discuss how to learn one. Once we have a model, it can be used for decision-time planning and improving the performance of model-free methods.

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