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Mastering Reinforcement Learning with Python

You're reading from  Mastering Reinforcement Learning with Python

Product type Book
Published in Dec 2020
Publisher Packt
ISBN-13 9781838644147
Pages 544 pages
Edition 1st Edition
Languages
Author (1):
Enes Bilgin Enes Bilgin
Profile icon Enes Bilgin

Table of Contents (24) Chapters

Preface 1. Section 1: Reinforcement Learning Foundations
2. Chapter 1: Introduction to Reinforcement Learning 3. Chapter 2: Multi-Armed Bandits 4. Chapter 3: Contextual Bandits 5. Chapter 4: Makings of a Markov Decision Process 6. Chapter 5: Solving the Reinforcement Learning Problem 7. Section 2: Deep Reinforcement Learning
8. Chapter 6: Deep Q-Learning at Scale 9. Chapter 7: Policy-Based Methods 10. Chapter 8: Model-Based Methods 11. Chapter 9: Multi-Agent Reinforcement Learning 12. Section 3: Advanced Topics in RL
13. Chapter 10: Introducing Machine Teaching 14. Chapter 11: Achieving Generalization and Overcoming Partial Observability 15. Chapter 12: Meta-Reinforcement Learning 16. Chapter 13: Exploring Advanced Topics 17. Section 4: Applications of RL
18. Chapter 14: Solving Robot Learning 19. Chapter 15: Supply Chain Management 20. Chapter 16: Personalization, Marketing, and Finance 21. Chapter 17: Smart City and Cybersecurity 22. Chapter 18: Challenges and Future Directions in Reinforcement Learning 23. Other Books You May Enjoy

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/
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Mastering Reinforcement Learning with Python
Published in: Dec 2020 Publisher: Packt ISBN-13: 9781838644147
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