Sutton, R. S. and Barto, A. G. (2018). Reinforcement learning: An introduction. The MIT Press, second edition. 1, 2.1
Tesauro, G. and Galperin, G. R. (1996). On-line policy improvement using monte-carlo search. In Mozer, M., Jordan, M. I., and Petsche, T., editors, Advances in Neural Information Processing Systems 9, pages 1068-1074. MIT Press. 4, B.1
Thakoor, S., Rowland, M., Borsa, D., Dabney, W., Munos, R., and Barreto, A. (2022). Generalised policy improvement with geometric policy composition. In Chaudhuri, K., Jegelka, S., Song, L., Szepesvari, C., Niu, G., and Sabato, S., editors, Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 21272-21307. PMLR. 1, 4, 5, C
Todorov, E., Erez, T., and Tassa, Y. (2012). Mujoco: A physics engine for model-based control. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 5026-5033, Vilamoura, Portugal. B.2
Tomov, M., Schulz, E., and Gershman, S. (2021). Multi-task reinforcement learning in humans. Nature Human Behaviour, pages 1-10. 5, C
Touati, A. and Ollivier, Y. (2021). Learning one representation to optimize all rewards. In Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., and Vaughan, J. W., editors, Advances in Neural Information Processing Systems, volume 34, pages 13-23. Curran Associates, Inc. 5, C
van Hasselt, H., Doron, Y., Strub, F., Hessel, M., Sonnerat, N., and Modayil, J. (2018). Deep reinforcement learning and the deadly triad. In Deep Reinforcement Learning Workshop, NeurIPS. 5, C
Van Seijen, H. and Sutton, R. S. (2013). Efficient planning in MDPs by small backups. In Proceedings of the 30th International Conference on International Conference on Machine Learning, ICML'13, pages III-361—III-369. JMLR.org. 2.3
Wan, Y., Rahimi-Kalahroudi, A., Rajendran, J., Momennejad, I., Chandar, S., and Van Seijen, H. H. (2022). Towards evaluating adaptivity of model-based reinforcement learning methods. In Chaudhuri, K., Jegelka, S., Song, L., Szepesvari, C., Niu, G., and Sabato, S., editors, Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 22536-22561. PMLR. 1, 2.3
Wu, P., Escontrela, A., Hafner, D., Abbeel, P., and Goldberg, K. (2022). Daydreamer: World models for physical robot learning. In Liu, K., Kulic, D., and Ichnowski, J., editors, Proceedings of The 6th Conference on Robot Learning, volume 205 of Proceedings of Machine Learning Research, pages 2226-2240. PMLR. 1
Wurman, P. R., Barrett, S., Kawamoto, K., MacGlashan, J., Subramanian, K., Walsh, T. J., Capobianco, R., Devlic, A., Eckert, F., Fuchs, F., Gilpin, L., Khandelwal, P., Kompella, V., Lin, H., MacAlpine, P., Oller, D., Seno, T., Sherstan, C., Thomure, M. D., Aghabozorgi, H., Barrett, L., Douglas, R., Whitehead, D., Durr, P., Stone, P., Spranger, M., and Kitano, H. (2022). Outracing champion gran turismo drivers with deep reinforcement learning. Nature, 602(7896):223-228. 1
Yao, H., Bhatnagar, S., Diao, D., Sutton, R. S., and Szepesvári, C. (2009). Multi-step dyna planning for policy evaluation and control. In Bengio, Y., Schuurmans, D., Lafferty, J., Williams, C., and Culotta, A., editors, Advances in Neural Information Processing Systems, volume 22. Curran Associates, Inc. 5, C
Yu, T., Kumar, A., Rafailov, R., Rajeswaran, A., Levine, S., and Finn, C. (2021). Combo: Conservative offline model-based policy optimization. In Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., and Vaughan, J. W., editors, Advances in Neural Information Processing Systems, volume 34, pages 28954-28967. Curran Associates, Inc. 4
Zahavy, T., Barreto, A., Mankowitz, D. J., Hou, S., O'Donoghue, B., Kemaev, I., and Singh, S. (2021). Discovering a set of policies for the worst case reward. In Proceedings of the 9th International Conference on Learning Representations. 5, C