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


  1. Calculate -step transition probabilities for the robot using the Markov chain model we introduced with the state initialized at . You will notice that it will take a bit more time for the system to reach the steady state.
  2. Modify the Markov chain to include the absorbing state for the robot crashing into the wall. What does your look like for a large ?
  3. Using the state values in Figure 4.7, calculate the value of a corner state using the estimates for the neighboring state values.
  4. Iteratively estimate the state values in the grid world MRP using matrix forms and operations instead of using a for loop.
  5. Calculate the action value where the policy π corresponds to taking no actions in any state using the values in Figure 4.7. Based on how compares to , would you consider changing your policy to take the action 'up' instead of no actions in state ?