[40] C. E. Luis, A. G. Bottero, J. Vinogradska, F. Berkenkamp, and J. Peters. Model-based uncertainty in value functions. arXiv preprint arXiv:2302.12526, 2023.
[41] P. Ménard, O. D. Domingues, X. Shang, and M. Valko. UCB momentum Q-learning: Correcting the bias without forgetting. In International Conference on Machine Learning, pages 7609-7618. PMLR, 2021.
[42] V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu. Asynchronous methods for deep reinforcement learning. In International conference on machine learning, pages 1928-1937. PMLR, 2016.
[43] M. Mutti, L. Pratissoli, and M. Restelli. Task-agnostic exploration via policy gradient of a non-parametric state entropy estimate. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 9028–9036, 2021.
[44] O. Nachum, M. Norouzi, K. Xu, and D. Schuurmans. Bridging the gap between value and policy based reinforcement learning. In Advances in Neural Information Processing Systems, pages 2772-2782, 2017.
[45] G. Neu, A. Jonsson, and V. Gomez. A unified view of entropy-regularized markov decision processes. arXiv preprint arXiv:1705.07798, 2017.
[46] B. O'Donoghue. Operator splitting for a homogeneous embedding of the linear complementarity problem. SIAM Journal on Optimization, 31(3):1999-2023, 2021.
[47] B. O'Donoghue. Variational Bayesian reinforcement learning with regret bounds. Advances in Neural Information Processing Systems, 34:28208-28221, 2021.
[48] B. O'Donoghue. On the connection between Bregman divergence and value in regularized markov decision processes. arXiv preprint arXiv:2210.12160, 2022.
[49] B. O'Donoghue. Efficient exploration via epistemic-risk-seeking policy optimization. arXiv preprint arXiv:2302.09339, 2023.
[50] B. O'Donoghue, E. Chu, N. Parikh, and S. Boyd. Conic optimization via operator splitting and homogeneous self-dual embedding. Journal of Optimization Theory and Applications, 169(3):1042-1068, June 2016.
[51] B. O'Donoghue and T. Lattimore. Variational Bayesian optimistic sampling. Advances in Neural Information Processing Systems, 34:12507-12519, 2021.
[52] B. O'Donoghue, T. Lattimore, and I. Osband. Matrix games with bandit feedback. In Uncertainty in Artificial Intelligence, pages 279–289. PMLR, 2021.
[53] B. O'Donoghue, I. Osband, and C. Ionescu. Making sense of reinforcement learning and probabilistic inference. In International Conference on Learning Representations, 2020.
[54] B. O'Donoghue, I. Osband, R. Munos, and V. Mnih. The uncertainty Bellman equation and exploration. In International Conference on Machine Learning, pages 3836-3845, 2018.
[55] I. Osband. Deep Exploration via Randomized Value Functions. PhD thesis, Stanford University, 2016.
[56] I. Osband, J. Aslanides, and A. Cassirer. Randomized prior functions for deep reinforcement learning. Advances in Neural Information Processing Systems, 31, 2018.
[57] I. Osband, C. Blundell, A. Pritzel, and B. Van Roy. Deep exploration via bootstrapped DQN. In Advances In Neural Information Processing Systems, pages 4026-4034, 2016.
[58] I. Osband, Y. Doron, M. Hessel, J. Aslanides, E. Sezener, A. Saraiva, K. McKinney, T. Lattimore, C. Szepesvari, S. Singh, et al. Behaviour suite for reinforcement learning. arXiv preprint arXiv:1908.03568, 2019.
[59] I. Osband, D. Russo, and B. Van Roy. (more) efficient reinforcement learning via posterior sampling. Advances in Neural Information Processing Systems, 26, 2013.