[2] David Auger, Adrien Couetoux, and Olivier Teytaud. Continuous upper confidence trees with polynomial exploration-consistency. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 194-209. Springer, 2013.
[3] Richard Bellman. A markovian decision process. Journal of mathematics and mechanics, 6(5):679-684, 1957.
[4] Blai Bonet and Hector Geffner. Labeled rtdp: Improving the convergence of real-time dynamic programming. In ICAPS, volume 3, pages 12-21, 2003.
[5] Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. Openai gym. arXiv preprint arXiv:1606.01540, 2016.
[6] Cameron B Browne, Edward Powley, Daniel Whitehouse, Simon M Lucas, Peter I Cowling, Philipp Rohlfshagen, Stephen Tavener, Diego Perez, Spyridon Samothrakis, and Simon Colton. A survey of monte carlo tree search methods. IEEE Transactions on Computational Intelligence and AI in games, 4(1):1-43, 2012.
[7] Sébastien Bubeck, Rémi Munos, and Gilles Stoltz. Pure exploration in multi-armed bandits problems. In International conference on Algorithmic learning theory, pages 23–37. Springer, 2009.
[8] Sébastien Bubeck, Rémi Munos, and Gilles Stoltz. Pure exploration in finitely-armed and continuous-armed bandits. Theoretical Computer Science, 412(19):1832–1852, 2011.
[9] Pierre-Arnaud Coquelin and Rémi Munos. Bandit algorithms for tree search. In Uncertainty in Artificial Intelligence, 2007.
[10] Tuan Q Dam, Carlo D'Eramo, Jan Peters, and Joni Pajarinen. Convex regularization in monte-carlo tree search. In International Conference on Machine Learning, pages 2365-2375. PMLR, 2021.
[11] Zohar Feldman and Carmel Domshlak. Monte-carlo planning: Theoretically fast convergence meets practical efficiency. arXiv preprint arXiv:1309.6828, 2013.
[12] Zohar Feldman and Carmel Domshlak. On mabs and separation of concerns in monte-carlo planning for mdps. In Twenty-Fourth International Conference on Automated Planning and Scheduling, 2014.
[13] Zohar Feldman and Carmel Domshlak. Simple regret optimization in online planning for markov decision processes. Journal of Artificial Intelligence Research, 51:165-205, 2014.
[14] Sylvain Gelly and David Silver. Combining online and offline knowledge in oct. In Proceedings of the 24th international conference on Machine learning, pages 273-280, 2007.
[15] Sylvain Gelly and David Silver. Monte-carlo tree search and rapid action value estimation in computer go. Artificial Intelligence, 175(11):1856-1875, 2011.
[16] Tuomas Haarnoja, Haoran Tang, Pieter Abbeel, and Sergey Levine. Reinforcement learning with deep energy-based policies. In International Conference on Machine Learning, pages 1352-1361. PMLR, 2017.
[17] 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.
[18] Eric A Hansen and Shlomo Zilberstein. Lao*: A heuristic search algorithm that finds solutions with loops. Artificial Intelligence, 129(1-2):35-62, 2001.
[19] Nicholas Hay, Stuart Russell, David Tolpin, and Solomon Eyal Shimony. Selecting computations: Theory and applications. arXiv preprint arXiv:1408.2048, 2014.
[20] Zohar Karnin, Tomer Koren, and Oren Somekh. Almost optimal exploration in multi-armed bandits. In International Conference on Machine Learning, pages 1238–1246. PMLR, 2013.
[21] Thomas Keller and Patrick Eyerich. Prost: Probabilistic planning based on uct. In Twenty-Second International Conference on Automated Planning and Scheduling, 2012.
[22] Levente Kocsis and Csaba Szepesvári. Bandit based monte-carlo planning. In European conference on machine learning, pages 282-293. Springer, 2006.
[23] Levente Kocsis, Csaba Szepesvári, and Jan Willemson. Improved monte-carlo search. Univ. Tartu, Estonia, Tech. Rep, 1, 2006.