W. Bradley Knox, Alessandro Allievi, Holger Banzhaf, Felix Schmitt, and Peter Stone. Reward (mis)design for autonomous driving. CoRR, abs/2104.13906, 2021. URL https://arxiv.org/abs/2104.13906.
Sergey Levine. Reinforcement learning and control as probabilistic inference: Tutorial and review. CoRR, abs/1805.00909, 2018. URL http://arxiv.org/abs/1805.00909.
Xiao-Yang Liu, Hongyang Yang, Jiechao Gao, and Christina Dan Wang. FinRL. In Proceedings of the Second ACM International Conference on AI in Finance. ACM, nov 2021. doi: 10.1145/3490354.3494366. URL https://doi.org/10.1145/2F3490354.3494366.
Yecheng Jason Ma, Jason Yan, Dinesh Jayaraman, and Osbert Bastani. How far i'll go: Offline goal-conditioned reinforcement learning via  $f$ -advantage regression, 2022. URL https://arxiv.org/abs/2206.03023.
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin A. Riedmiller. Playing atari with deep reinforcement learning. CoRR, abs/1312.5602, 2013. URL http://arxiv.org/abs/1312.5602.
Ofir Nachum, Yinlam Chow, Bo Dai, and Lihong Li. Dualdice: Behavior-agnostic estimation of discounted stationary distribution corrections. ArXiv, abs/1906.04733, 2019.
A. Ng, Daishi Harada, and Stuart J. Russell. Policy invariance under reward transformations: Theory and application to reward shaping. In International Conference on Machine Learning, 1999.
Tianwei Ni, Harshit S. Sikchi, Yufei Wang, Tejus Gupta, Lisa Lee, and Benjamin Eysenbach. f-irl: Inverse reinforcement learning via state marginal matching. CoRR, abs/2011.04709, 2020. URL https://arxiv.org/abs/2011.04709.
Scott Niekum. Evolved intrinsic reward functions for reinforcement learning. Proceedings of the AAAI Conference on Artificial Intelligence, 24(1):1955-1956, Jul. 2010. doi: 10.1609/aaai.v24i1.7772. URL https://ojs.aaai.org/index.php/AAAI/article/view/7772.
OpenAI, Matthias Plappert, Raul Sampedro, Tao Xu, Ilge Akkaya, Vineet Kosaraju, Peter Welinder, Ruben D'Sa, Arthur Petron, Henrique Ponde de Oliveira Pinto, Alex Paino, Hyeonwoo Noh, Lilian Weng, Qiming Yuan, Casey Chu, and Wojciech Zaremba. Asymmetric self-play for automatic goal discovery in robotic manipulation. CoRR, abs/2101.04882, 2021. URL https://arxiv.org/abs/2101.04882.
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. Training language models to follow instructions with human feedback, 2022.
Matthias Plappert, Marcin Andrychowicz, Alex Ray, Bob McGrew, Bowen Baker, Glenn Powell, Jonas Schneider, Josh Tobin, Maciek Chogiej, Peter Welinder, Vikash Kumar, and Wojciech Zaremba. Multi-goal reinforcement learning: Challenging robotics environments and request for research, 2018.
Yury Polyanskiy and Yihong Wu. "Information Theory From Coding to Learning". Cambridge University Press, 2022. URL https://people.lids.mit.edu/yp/homepage/data/ itbook-export.pdf.
Doina Precup. Eligibility traces for off-policy policy evaluation. Computer Science Department Faculty Publication Series, pp. 80, 2000.
Martin L Puterman. Markov decision processes. *Handbooks in operations research and management science*, 2:331-434, 1990.
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. CoRR, abs/1707.06347, 2017. URL http://arxiv.org/abs/1707.06347.