Recommended Reading
Abbeel, P., Coates, A., Quigley, M., & Ng, A. Y. (2007). An application of reinforcement learning to aerobatic helicopter flight. In Advances in neural information processing systems (Vol. 19, pp. 1–8). Cambridge, MA: MIT Press.
Abbeel, P., Quigley, M., & Ng, A. Y. (2006). Using inaccurate models in reinforcement learning. In Proceedings of the 23rd international conference on machine learning (pp. 1–8). ACM Press, New York, USA.
Atkeson, C. G., & Santamaria, J. C. (1997). A comparison of direct and model-based reinforcement learning. In Proceedings of the international conference on robotics and automation (pp. 20–25). IEEE Press.
Atkeson, C. G., & Schaal, S. (1997). Robot learning from demonstration. In Proceedings of the fourteenth international conference on machine learning (Vol. 4, pp. 12–20). San Francisco: Morgan Kaufmann.
Barto, A. G., Bradtke, S. J., & Singh, S. P. (1995). Learning to act using real-time dynamic programming. Artificial Intelligence, 72(1), 81–138.
Baxter, J., Tridgell, A., & Weaver, L. (1998). TDLeaf(λ): Combining temporal difference learning with game-tree search. In Proceedings of the ninth Australian conference on neural networks (ACNN’98) (pp. 168–172).
Brafman, R. I., & Tennenholtz, M. (2002). R-MAX – a general polynomial time algorithm for near-optimal reinforcement learning. Journal of Machine Learning Research, 2, 213–231.
Kaelbling, L. P., Littman, M. L., & Moore, A. P. (1996). Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4, 237–285.
Kearns, M., & Singh, S. (2002). Near-optimal reinforcement learning in polynomial time. Machine Learning, 49(2/3), 209–232.
Moore, A. W., & Atkeson, C. G. (1993). Prioritized sweeping: Reinforcement learning with less data and less real time. Machine Learning, 13, 103–130.
Peng, J., & Williams, R. J. (1993). Efficient learning and planning within the dyna framework. Adaptive Behavior, 1(4), 437–454.
Puterman, M. L. (1994). Markov decision processes: Discrete dynamic stochastic programming. New York: Wiley.
Schaal, S., & Atkeson, C. G. (1994). Robot juggling: Implementation of memory-based learning. IEEE Control Systems Magazine, 14(1), 57–71.
Singh, S., Kearns, M., Litman, D., & Walker, M. (1999) Reinforcement learning for spoken dialogue systems. In Advances in neural information processing systems (Vol. 11, pp. 956–962). MIT Press.
Sutton, R. S. (1990). Integrated architectures for learning, planning, and reacting based on approximating dynamic programming. In Proceedings of the seventh international conference on machine learning (pp. 216–224). San Francisco: Morgan Kaufmann.
Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: An introduction. Cambridge, MA: MIT Press.
Tadepalli, P., & Ok, D. (1998). Model-based average-reward reinforcement learning. Artificial Intelligence, 100, 177–224.
Tesauro, G. (1995). Temporal difference learning and TD-Gammon. Communications of the ACM, 38(3), 58–68.
Wang, X., & Dietterich, T. G. (2003). Model-based policy gradient reinforcement learning. In Proceedings of the 20th international conference on machine learning (pp. 776–783). AAAI Press.
Wilson, A., Fern, A., Ray, S., & Tadepalli, P. (2007). Multi-task reinforcement learning: A hierarchical Bayesian approach. In Proceedings of the 24th international conference on machine learning (pp. 1015–1022). Madison, WI: Omnipress.
Zhang, W., & Dietterich, T. G. (1995). A reinforcement learning approach to job-shop scheduling. In Proceedings of the international joint conference on artificial intelligence (pp. 1114–1120). Morgan Kaufman.
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Ray, S., Tadepalli, P. (2011). Model-Based Reinforcement Learning. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_556
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DOI: https://doi.org/10.1007/978-0-387-30164-8_556
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