[26] Vladimir G Vovk. Prediction of stochastic sequences. Rossiiskaya Akademiya Nauk. Problemy Peredachi Informatsii, 25(4):35-49, 1989.
[27] Ming Li, Paul Vitányi, et al. An introduction to Kolmogorov complexity and its applications, volume 3. Springer, 2008.
[28] Jan Leike, Tor Lattimore, Laurent Orseau, and Marcus Hutter. On Thompson sampling and asymptotic optimality. In Proc. 26th International Joint Conf. on Artificial Intelligence (IJ-CAI'17), pages 4889-4893, Melbourne, Australia, 2017. ISBN 978-0-9992411-0-3. doi: 10.24963/ijcai.2017/688. URL http://arxiv.org/abs/1602.07905. Best sister conferences paper track.
[29] Jan Leike and Marcus Hutter. Bad universal priors and notions of optimality. Journal of Machine Learning Research, W&CP: COLT, 40:1244-1259, 2015. ISSN 1532-4435. URL http://arxiv.org/abs/1510.04931. Also presented at EWRL'15. http://ewrl.files.wordpress.com/2015/02/ewrl12_2015 Submission_3.pdf.
[30] Tor Lattimore and Marcus Hutter. Asymptotically optimal agents. In Proc. 22nd International Conf. on Algorithmic Learning Theory (ALT'11), volume 6925 of LNAI, pages 368-382, Espoo, Finland, 2011. Springer. ISBN 3-642-24411-4. doi: 10.1007/978-3-642-24412-4_29. URL http://arxiv.org/abs/1107.5537.
[31] Michael K Cohen, Elliot Catt, and Marcus Hutter. Curiosity killed or incapacitated the cat and the asymptotically optimal agent. IEEE Journal on Selected Areas in Information Theory, 2(2): 665-677, 2021.
[32] D. Blackwell and L. Dubins. Merging of opinions with increasing information. Annals of Mathematical Statistics, 33:882-887, 1962.
[33] Shane Legg and Marcus Hutter. Ergodic mdps admit self-optimising policies. Technical report, Technical Report IDSIA-21-04, IDSIA, 2004.
[34] Joel Veness. Approximate universal artificial intelligence and self-play learning for games. PhD thesis, University of New South Wales, Sydney, Australia, 2011.
[35] L. Orseau. Universal knowledge-seeking agents. In Proc. 22nd International Conf. on Algorithmic Learning Theory (ALT'11), volume 6925 of LNAI, pages 353-367, Espoo, Finland, 2011. Springer. doi: 10.1007/978-3-642-24412-4_28.
[36] Laurent Orseau, Tor Lattimore, and Marcus Hutter. Universal knowledge-seeking agents for stochastic environments. In Proc. 24th International Conf. on Algorithmic Learning Theory (ALT'13), volume 8139 of LNAI, pages 158-172, Singapore, 2013. Springer. ISBN 978-3-642-40934-9. doi: 10.1007/978-3-642-40935-6_12.
[37] Tor Lattimore and Marcus Hutter. Bayesian reinforcement learning with exploration. In Proc. 25th International Conf. on Algorithmic Learning Theory (ALT'14), volume 8776 of LNAI, pages 170-184, Bled, Slovenia, 2014. Springer. ISBN 978-3-319-11661-7. doi: 10.1007/978-3-319-11662-4_13.
[38] T. Lattimore. Theory of General Reinforcement Learning. PhD thesis, Research School of Computer Science, Australian National University, 2014. URL https://library.anu.edu.au/record=b3579030.
[39] William R Thompson. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika, 25(3/4):285-294, 1933.
[40] Michael Cohen, Elliot Catt, and Marcus Hutter. A strongly asymptotically optimal agent in general environments. In Proc. 28th International Joint Conf. on Artificial Intelligence (IJCAI'19), pages 2179-2186, Macao, China, 2019. ISBN 978-0-9992411-4-1. doi: 10.24963/ ijcai.2019/302. URL http://arxiv.org/abs/1903.01021.
[41] Joel Veness, Kee Siong Ng, Marcus Hutter, William Uther, and David Silver. A monte-carlo aixi approximation. Journal of Artificial Intelligence Research, 40:95-142, 2011.