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. Sutton, R. S., Barto, A. G. (2018). RL: An Introduction. The MIT Press.
  2. Tesauro, G. (1992). Practical issues in temporal difference learning. ML 8, 257–277.
  3. Tesauro, G. (1995). Temporal difference learning and TD-Gammon. Commun. ACM 38, 3, 58-68. 
  4. Silver, D. (2018). Success Stories of Deep RL. Retrieved from https://youtu.be/N8_gVrIPLQM.
  5. Crites, R. H., Barto, A.G. (1995). Improving elevator performance using RL. In Proceedings of the 8th International Conference on Neural Information Processing Systems (NIPS'95).
  6. Mnih, V. et al. (2015). Human-level control through deep RL. Nature, 518(7540), 529–533.
  7. Silver, D. et al. (2018). A general RL algorithm that masters chess, shogi, and Go through self-play. Science, 362(6419), 1140–1144.
  8. Vinyals, O. et al. (2019). Grandmaster level in StarCraft II using multi-agent RL.
  9. OpenAI. (2018). OpenAI Five. Retrieved from https://blog.openai.com/openai-five/.
  10. Heess, N. et al. (2017). Emergence of Locomotion Behaviours in Rich Environments. ArXiv, abs/1707.02286.
  11. OpenAI et al. (2018). Learning Dexterous In-Hand Manipulation. ArXiv, abs/1808.00177.
  12. OpenAI et al. (2019). Solving Rubik's Cube with a Robot Hand. ArXiv, abs/1910.07113.
  13. OpenAI Blog (2019). Solving Rubik's Cube with a Robot Hand. URL: https://openai.com/blog/solving-rubiks-cube/
  14. Zheng, G. et al. (2018). DRN: A Deep RL Framework for News Recommendation. In Proceedings of the 2018 World Wide Web Conference (WWW '18). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 167–176. DOI: https://doi.org/10.1145/3178876.3185994
  15. Chandrashekar, A. et al. (2017). Artwork Personalization at Netflix. The Netflix Tech Blog. URL: https://medium.com/netflix-techblog/artwork-personalization-c589f074ad76
  16. McKinney, S. M. et al. (2020). International evaluation of an AI system for breast cancer screening. Nature, 89-94.
  17. Agrawal, R. (2018, March 8). Microsoft News Center India. Retrieved from https://news.microsoft.com/en-in/features/microsoft-ai-network-healthcare-apollo-hospitals-cardiac-disease-prediction/