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)
1
Section 1: Reinforcement Learning Foundations
7
Section 2: Deep Reinforcement Learning
12
Section 3: Advanced Topics in RL
17
Section 4: Applications of RL

References

  1. Bouneffouf, D., & Rish, I. (2019). A Survey on Practical Applications of Multi-Armed and Contextual Bandits. Retrieved from arXiv: https://arxiv.org/abs/1904.10040
  2. Chandrashekar, A., Amat, F., Basilico, J., & Jebara, T. (2017, December 7). Netflix Technology Blog. Retrieved from Artwork Personalization at Netflix: https://netflixtechblog.com/artwork-personalization-c589f074ad76
  3. Chapelle, O., & Li, L. (2011). An Empirical Evaluation of Thompson Sampling. Advances in Neural Information Processing Systems 24, (pp. 2249-2257).
  4. Collier, M., & Llorens, H. U. (2018). Deep Contextual Multi-Armed Bandits. Retrieved from arXiv: https://arxiv.org/abs/1807.09809
  5. Gal, Y., Hron, J., & Kendall, A. (2017). Concrete Dropout. Advances in Neural Information Processing Systems 30, (pp. 3581-3590).
  6. Marmerola, G. D. (2017, November 28). Thompson Sampling for Contextual bandits. Retrieved from Guilherme's Blog: https://gdmarmerola.github.io/ts...