[16] Aurélien Garivier and Emilie Kaufmann. Optimal best arm identification with fixed confidence. In Conference on Learning Theory, pages 998-1027. PMLR, 2016.
[17] Mohammad Gheshlaghi Azar, Rémi Munos, and Hilbert J Kappen. Minimax pac bounds on the sample complexity of reinforcement learning with a generative model. Machine learning, Springer, 91:325-349, 2013.
[18] Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine. Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. In International Conference on Machine Learning, pages 1861-1870. PMLR, 2018.
[19] Chi Jin, Zeyuan Allen-Zhu, Sebastien Bubeck, and Michael I Jordan. Is q-learning provably efficient? Advances in Neural Information Processing Systems (NeurIPS), 31, 2018.
[20] Sham Machandranath Kakade. On the sample complexity of reinforcement learning. University of London, University College London (United Kingdom), 2003.
[21] Emilie Kaufmann, Olivier Cappé, and Aurélien Garivier. On the complexity of best arm identification in multi-armed bandit models. Journal of Machine Learning Research, 17:1–42, 2016.
[22] Michael Kearns and Satinder Singh. Near-optimal reinforcement learning in polynomial time. Machine learning, Springer, 49:209-232, 2002.
[23] Johannes Kirschner and Andreas Krause. Information directed sampling and bandits with heteroscedastic noise. In Conference On Learning Theory, pages 358-384. PMLR, 2018.
[24] Pawel Ladosz, Lilian Weng, Minwoo Kim, and Hyondong Oh. Exploration in deep reinforcement learning: A survey. Information Fusion, 2022.
[25] Tor Lattimore and Marcus Hutter. Pac bounds for discounted mdps. In Algorithmic Learning Theory: 23rd International Conference, ALT 2012, Lyon, France, October 29-31, 2012. Proceedings 23, pages 320-334. Springer, 2012.
[26] Tor Lattimore and Csaba Szepesvári. Bandit algorithms. Cambridge University Press, 2020.
[27] Kimin Lee, Michael Laskin, Aravind Srinivas, and Pieter Abbeel. Sunrise: A simple unified framework for ensemble learning in deep reinforcement learning. In International Conference on Machine Learning, pages 6131-6141. PMLR, 2021.
[28] Aymen Al Marjani and Alexandre Proutiere. Adaptive sampling for best policy identification in markov decision processes. In International Conference on Machine Learning, pages 7459-7468. PMLR, 2021.
[29] Aymen Al Marjani, Aurélien Garivier, and Alexandre Proutiere. Navigating to the best policy in markov decision processes. In Advances in Neural Information Processing Systems (NeurIPS), 2021.
[30] Borislav Mavrin, Hengshuai Yao, Linglong Kong, Kaiwen Wu, and Yaoliang Yu. Distributional reinforcement learning for efficient exploration. In International Conference on Machine Learning, pages 4424-4434. PMLR, 2019.
[31] 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.
[32] Thomas M Moerland, Joost Broekens, and Catholijn M Jonker. Efficient exploration with double uncertain value networks. In Deep Reinforcement Learning Symposium, NIPS 2017, 2017.
[33] Nikolay Nikolov, Johannes Kirschner, Felix Berkenkamp, and Andreas Krause. Information-directed exploration for deep reinforcement learning. In International Conference on Learning Representations, 2018.