01) Rainbow: Combining Improvements in Deep Reinforcement Learning
    Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg
    Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, David
    Silver
    2017
02) Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?
    Maithra Raghu, Alex Irpan, Jacob Andreas, Robert Kleinberg, Quoc
    V. Le, Jon Kleinberg
    2017
03) Mastering Chess and Shogi by Self-Play with a General Reinforcement
    Learning Algorithm
    David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis
    Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre,
    Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan,
    Demis Hassabis
    2017
04) An Emphatic Approach to the Problem of Off-policy
    Temporal-Difference Learning
    Richard S. Sutton, A. Rupam Mahmood, Martha White
    2015
05) Safe and Efficient Off-Policy Reinforcement Learning
    Rémi Munos, Tom Stepleton, Anna Harutyunyan, Marc G. Bellemare
    2016
06) Convergence of stochastic iterative dynamic programming algorithms
    T Jaakkola, MI Jordan, SP Singh
    1993
07) Deep reinforcement learning from human preferences
    Paul Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg,
    Dario Amodei
    2017
08) Neuro-dynamic Programming, Chap 7
    Dimitri Bertsekas, John Tsitsiklis
    1996
09) Montezuma’s Revenge Solved by Go-Explore, a New Algorithm for
    Hard-Exploration Problems (Sets Records on Pitfall, Too)
    Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O. Stanley, and Jeff Clune
    2018
10) From Bandits to Monte-Carlo Tree Search: The Optimistic Principle
    Applied to Optimization and Planning
    Rémi Munos
    2014