[16] Dimitri P Bertsekas. Rollout algorithms for discrete optimization: A survey. Handbook of Combinatorial Optimization, D. Zu and P. Pardalos, Eds. Springer, 2010.
[17] Gerald Tesauro. TD-Gammon, a self-teaching backgammon program, achieves master-level play. Neural computation, 6(2):215-219, 1994.
[18] David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al. A general reinforcement learning algorithm that masters chess, shogi, and go through self-play. Science, 362(6419):1140-1144, 2018.
[19] David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al. Mastering the game of go with deep neural networks and tree search. nature, 529(7587):484-489, 2016.
[20] Matteo Hessel, Joseph Modayil, Hado Van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, and David Silver. Rainbow: Combining improvements in deep reinforcement learning. In Thirty-second AAAI conference on artificial intelligence, 2018.
[21] John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel. High-dimensional continuous control using generalized advantage estimation. arXiv preprint arXiv:1506.02438, 2015.
[22] Jingkang Wang, Yang Liu, and Bo Li. Reinforcement learning with perturbed rewards. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 6202-6209, 2020.
[23] Fabio Pardo. Tonic: A deep reinforcement learning library for fast prototyping and benchmarking. arXiv preprint arXiv:2011.07537, 2020.
[24] Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu. Asynchronous methods for deep reinforcement learning. In International conference on machine learning, pages 1928-1937. PMLR, 2016.
[25] Yonathan Efroni, Gal Dalal, Bruno Scherrer, and Shie Mannor. Beyond the one-step greedy approach in reinforcement learning. In International Conference on Machine Learning, pages 1387-1396. PMLR, 2018.
[26] Christopher John Cornish Hellaby Watkins. Learning from delayed rewards. 1989.
[27] Jing Peng and Ronald J Williams. Incremental multi-step q-learning. In Machine Learning Proceedings 1994, pages 226-232. Elsevier, 1994.
[28] J Fernando Hernandez-Garcia and Richard S Sutton. Understanding multi-step deep reinforcement learning: a systematic study of the dqn target. arXiv preprint arXiv:1901.07510, 2019.
[29] Harm Van Seijen, Mehdi Fatemi, and Arash Tavakoli. Using a logarithmic mapping to enable lower discount factors in reinforcement learning. Advances in Neural Information Processing Systems, 32, 2019.
[30] Harm Van Seijen and Richard S Sutton. Efficient planning in MDPs by small backups. In Proc. 30th Int. Conf. Mach. Learn, volume 28. Citeseer, 2013.
[31] Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, and Steffen Udluft. Learning and policy search in stochastic dynamical systems with bayesian neural networks. arXiv preprint arXiv:1605.07127, 2016.
[32] David Silver, Hado Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David Reichert, Neil Rabinowitz, Andre Barreto, et al. The predictron: End-to-end learning and planning. In International Conference on Machine Learning, pages 3191-3199. PMLR, 2017.