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

Deep Q-networks

DQN is a seminal work by (Mnih et al., 2015) that made deep RL a viable approach to complex sequential control problems. The authors demonstrated that a single DQN architecture can achieve super-human level performance in many Atari games without any feature engineering, which created a lot of excitement regarding the progress of AI. Let's look into what makes DQN so effective compared to the algorithms we mentioned earlier.

Key concepts in deep Q-networks

DQN modifies online Q-learning with two important concepts by using experience replay and a target network, which greatly stabilize the learning. We describe these concepts next.

Experience replay

As mentioned earlier, simply using the experience sampled sequentially from the environment leads to highly correlated gradient steps. DQN, on the other hand, stores those experience tuples, , in a replay buffer (memory), an idea that was introduced back in 1993 (Lin, 1993). During learning, the samples...