Tiancheng Jin and Haipeng Luo. Simultaneously learning stochastic and adversarial episodic mdps with known transition. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, 2020.
Tiancheng Jin, Longbo Huang, and Haipeng Luo. The best of both worlds: stochastic and adversarial episodic mdps with unknown transition. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, pages 20491-20502, 2021b.
Sham M. Kakade and John Langford. Approximately optimal approximate reinforcement learning. In Claude Sammut and Achim G. Hoffmann, editors, Machine Learning, Proceedings of the Nineteenth International Conference (ICML 2002), University of New South Wales, Sydney, Australia, July 8-12, 2002, pages 267-274. Morgan Kaufmann, 2002.
Fang Kong, Xiangcheng Zhang, Baoxiang Wang, and Shuai Li. Improved regret bounds for linear adversarial mdps via linear optimization. CoRR, abs/2302.06834, 2023.
Michael Laskin, Aravind Srinivas, and Pieter Abbeel. CURL: contrastive unsupervised representations for reinforcement learning. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, volume 119 of Proceedings of Machine Learning Research, pages 5639-5650. PMLR, 2020.
Tor Lattimore and Csaba Szepesvári. Bandit algorithms. Cambridge University Press, 2020.
Haipeng Luo, Chen-Yu Wei, and Chung-Wei Lee. Policy optimization in adversarial mdps: Improved exploration via dilated bonuses. CoRR, abs/2107.08346, 2021a.
Haipeng Luo, Chen-Yu Wei, and Chung-Wei Lee. Policy optimization in adversarial mdps: Improved exploration via dilated bonuses. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, pages 22931-22942, 2021b.
Yifei Min, Jiafan He, Tianhao Wang, and Quanquan Gu. Learning stochastic shortest path with linear function approximation. In International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA, volume 162 of Proceedings of Machine Learning Research, pages 15584-15629. PMLR, 2022.
Aditya Modi, Jinglin Chen, Akshay Krishnamurthy, Nan Jiang, and Alekh Agarwal. Model-free representation learning and exploration in low-rank mdps. CoRR, abs/2102.07035, 2021.
Gergely Neu and Julia Olkhovskaya. Online learning in mdps with linear function approximation and bandit feedback. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, pages 10407-10417, 2021.
Gergely Neu, András György, and Csaba Szepesvári. The online loop-free stochastic shortest-path problem. In Adam Tauman Kalai and Mehryar Mohri, editors, COLT 2010 - The 23rd Conference on Learning Theory, Haifa, Israel, June 27-29, 2010, pages 231-243. Omnipress, 2010a.
Gergely Neu, András György, Csaba Szepesvári, and András Antos. Online markov decision processes under bandit feedback. In Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, Vancouver, British Columbia, Canada, pages 1804-1812. Curran Associates, Inc., 2010b.
Gergely Neu, András György, and Csaba Szepesvári. The adversarial stochastic shortest path problem with unknown transition probabilities. In Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2012, La Palma, Canary Islands, Spain, April 21-23, 2012, volume 22 of JMLR Proceedings, pages 805-813. JMLR.org, 2012.
Chengzhuo Ni, Yuda Song, Xuezhou Zhang, Chi Jin, and Mengdi Wang. Representation learning for general-sum low-rank markov games. CoRR, abs/2210.16976, 2022.