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 12: Meta-Reinforcement Learning

Humans learn new skills from much fewer data compared to a reinforcement learning agent. Two factors contributing to this are, first, we come with priors in our brains at birth that give us certain capabilities from the get-go; and second, we are able to transfer our knowledge from one skill to another quite efficiently and adapt to new environments fast. Meta-reinforcement learning aims to achieve a similar capability for artificial intelligence agents. In this chapter, we describe what meta-reinforcement learning is, the approaches it uses, and the challenges it faces. Specifically, we cover the following topics:

  • Introducing meta-reinforcement learning
  • Meta-reinforcement learning with recurrent policies
  • Gradient-based meta-reinforcement learning
  • Meta-reinforcement learning as partially observed reinforcement learning
  • Challenges in meta-reinforcement learning