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) Proximal Policy Optimization Algorithms 
    John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov
    2017
03) Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
    Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, Sergey Levine
    2018
04) 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
05) Convergence of stochastic iterative dynamic programming algorithms
    T Jaakkola, MI Jordan, SP Singh
    1993
06) Neuro-dynamic Programming, Chap 7
    Dimitri Bertsekas, John Tsitsiklis
    1996
07) From Bandits to Monte-Carlo Tree Search: The Optimistic Principle
    Applied to Optimization and Planning
    Rémi Munos
    2014
08) Deep reinforcement learning from human preferences
    Paul Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg,
    Dario Amodei
    2017
09) Direct Preference Optimization: Your Language Model is Secretly a Reward Model
    Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, Chelsea Finn
    2023
10) Deep learning, reinforcement learning, and world models
    Yutaka Matsuo, Yann LeCun, Maneesh Sahani, Doina Precup, David Silver, Masashi Sugiyama, Eiji Uchibe, Jun Morimoto
    2021