Ioannis Kourouklides

This page contains resources about Reinforcement Learning.

Subfields and Concepts[]

  • Multi-Armed Bandit
  • Finite Markov Decision Process
  • Temporal-Difference Learning
  • Q-Learning
  • Adaptive Dynamic Programming
  • Deep Reinforcement Learning
  • Connectionist Reinforcement Learning
    • Score function estimator / REINFORCE
  • Variance Teduction Techniques (VRT) for gradient estimates

Online Courses[]

Video Lectures[]

Lectures Notes[]

Books and Book Chapters[]

  • Russell, S. J., & Norvig, P. (2010). "Chapter 21: Reinforcement Learning". Artificial Intelligence: A Modern Approach. Prentice Hall.
  • Alpaydin, E. (2010). "Chapter 18: Reinforcement Learning". Introduction to machine learning. MIT Press.
  • Sutton, R. S., & Barto, A. G. (1998). Reinforcement Learning: An Introduction. MIT Press.

Scholarly Articles[]

  • Szepesvári, C. (2010). Algorithms for Reinforcement Learning. Synthesis Lectures on Artificial Intelligence and Machine 4(1), 1-103.
  • Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement Learning: A Survey. Journal of Artificial Intelligence Research4, 237-285.
  • Mitchell, T. M. (1997). "Chapter 13: Reinforcement Learning". Machine Learning. McGraw Hill.
  • Wl, M. H., Harmon, M. E., & Harmon, S. S. (1996). Reinforcement Learning: A Tutorial.



See also[]

Other resources[]