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

Revisiting off-policy Methods

One of the challenges with policy-based methods is that they are on-policy, which requires collecting new samples after every policy update. If it is costly to collect samples from the environment, then training on-policy methods could be really expensive. On the other hand, the value-based methods we covered in the previous chapter are off-policy but they only work with discrete action spaces. Therefore, there is a need for a class of methods that work with continuous action spaces and off-policy. In this section, we cover such algorithms. Let's start with the first one: Deep Deterministic Policy Gradient.

DDPG: Deep Deterministic Policy Gradient

DDPG, in some sense, is an extension of deep Q-learning to continuous action spaces. Remember that deep Q-learning methods learn a representation for action values, . The best action is then given by in a given state . Now, if the action space is continuous, learning the action-value representation...