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Published inDec 2020
Reading LevelBeginner
PublisherPackt
ISBN-139781838644147
Edition1st Edition
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Python
Tools
PyTorch
Concepts
Reinforcement Learning
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Enes Bilgin
Enes Bilgin
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Enes Bilgin

Enes Bilgin works as a senior AI engineer and a tech lead in Microsoft's Autonomous Systems division. He is a machine learning and operations research practitioner and researcher with experience in building production systems and models for top tech companies using Python, TensorFlow, and Ray/RLlib. He holds an M.S. and a Ph.D. in systems engineering from Boston University and a B.S. in industrial engineering from Bilkent University. In the past, he has worked as a research scientist at Amazon and as an operations research scientist at AMD. He also held adjunct faculty positions at the McCombs School of Business at the University of Texas at Austin and at the Ingram School of Engineering at Texas State University.
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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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Author (1)

author image
Enes Bilgin

Enes Bilgin works as a senior AI engineer and a tech lead in Microsoft's Autonomous Systems division. He is a machine learning and operations research practitioner and researcher with experience in building production systems and models for top tech companies using Python, TensorFlow, and Ray/RLlib. He holds an M.S. and a Ph.D. in systems engineering from Boston University and a B.S. in industrial engineering from Bilkent University. In the past, he has worked as a research scientist at Amazon and as an operations research scientist at AMD. He also held adjunct faculty positions at the McCombs School of Business at the University of Texas at Austin and at the Ingram School of Engineering at Texas State University.
Read more about Enes Bilgin

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