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

Distributed deep Q-learning

Deep learning models are notorious for their hunger for data. When it comes to reinforcement learning, the hunger for data is much greater, which mandates parallelization for data collection while training RL models. The original DQN model is a single-threaded process. Despite its great success, it has limited scalability. In this section, we present methods to parallelize deep Q-learning to many (possibly thousands) of processes.

The key insight behind distributed Q-learning is its off-policy nature, which virtually decouples the training from experience generation. In other words, the specific processes/policies that generate the experience do not matter to the training process (although there are caveats to this statement). Combined with the idea of using a replay buffer, this allows us to parallelize the experience generation and store the data in central or distributed replay buffers. In addition, we can parallelize how the data is sampled from these...