[34] Anusha Nagabandi et al. "Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning". In: 2018 IEEE international conference on robotics and automation (ICRA). IEEE. 2018, pp. 7559-7566.
[35] Ingram Olkin and Friedrich Pukelsheim. “The distance between two random vectors with given dispersion matrices”. In: Linear Algebra and its Applications 48 (1982), pp. 257–263.
[36] Ian Osband and Benjamin Van Roy. "Model-based reinforcement learning and the eluder dimension". In: Advances in Neural Information Processing Systems 27 (2014).
[37] Ian Osband et al. "Deep exploration via bootstrapped DQN". In: Advances in neural information processing systems 29 (2016).
[38] Feiyang Pan et al. "Trust the model when it is confident: Masked model-based actor-critic". In: Advances in neural information processing systems 33 (2020), pp. 10537-10546.
[39] Julian Schrittwieser et al. "Online and offline reinforcement learning by planning with a learned model". In: Advances in Neural Information Processing Systems 34 (2021), pp. 27580-27591.
[40] John Schulman et al. "Proximal policy optimization algorithms". In: arXiv preprint arXiv:1707.06347 (2017).
[41] John Schulman et al. "Trust region policy optimization". In: International conference on machine learning. PMLR. 2015, pp. 1889-1897.
[42] David Silver et al. “A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play”. In: Science 362.6419 (2018), pp. 1140–1144.
[43] Wen Sun et al. "Dual policy iteration". In: Advances in Neural Information Processing Systems 31 (2018).
[44] Richard S Sutton. "Dyna, an integrated architecture for learning, planning, and reacting". In: ACM Sigart Bulletin 2.4 (1991), pp. 160-163.
[45] Richard S Sutton. "Integrated architectures for learning, planning, and reacting based on approximating dynamic programming". In: Machine learning proceedings 1990. Elsevier, 1990, pp. 216-224.
[46] Emanuel Todorov, Tom Erez, and Yuval Tassa. "Mujoco: A physics engine for model-based control". In: 2012 IEEE/RSJ international conference on intelligent robots and systems. IEEE. 2012, pp. 5026-5033.
[47] Leonid Nisonovich Vaserstein. "Markov processes over denumerable products of spaces, describing large systems of automata". In: Problemy Peredachi Informatsii 5.3 (1969), pp. 64-72.
[48] Arun Venkatraman, Martial Hebert, and J Bagnell. "Improving multi-step prediction of learned time series models". In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 29. 1. 2015.
[49] Tingwu Wang and Jimmy Ba. "Exploring Model-based Planning with Policy Networks". In: International Conference on Learning Representations. 2020. URL: https : / / openreview.net/forum?id=H1exf64KwH.
[50] Xiyao Wang et al. "Live in the moment: Learning dynamics model adapted to evolving policy". In: International Conference on Machine Learning. PMLR. 2023, pp. 36470-36493.
[51] Zhihai Wang et al. “Sample-Efficient Reinforcement Learning via Conservative Model-Based Actor-Critic”. In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 36. 8. 2022, pp. 8612–8620.
[52] Jingda Wu, Zhiyu Huang, and Chen Lv. "Uncertainty-aware model-based reinforcement learning: methodology and application in autonomous driving". In: IEEE Transactions on Intelligent Vehicles (2022).
[53] Zifan Wu et al. "Plan To Predict: Learning an Uncertainty-Foreseeing Model for Model-Based Reinforcement Learning". In: Advances in Neural Information Processing Systems 35 (2022), pp. 15849-15861.
[54] Shenao Zhang. "Conservative Dual Policy Optimization for Efficient Model-Based Reinforcement Learning". In: Advances in Neural Information Processing Systems 35 (2022), pp. 25450-25463.