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There are over 15 distinct communities that work in the general area of sequential decisions and information, often referred to as decisions under uncertainty or stochastic optimization.
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Bertsekas, D. P. & Tsitsiklis, J. N. (1996), Neuro-Dynamic Programming
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Kothare, M. V., Balakrishnan, V. & Morari, M. (1996), ‘Robust constrained model predictive control using linear matrix inequalities’, Automatica
1996
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Bertsekas, D. P., Tsitsiklis, J. N. & Wu, C. (1997), ‘Rollout Algorithms for Combinatorial Optimization’, Journal of Heuristics
1997
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Sontag, E. (1998), ‘Mathematical Control Theory, 2nd ed.’, Springer
1998
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Sutton, R. S. & Barto, A. G. (1998), Reinforcement Learning: An Introduction
1998
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Murray, J. J., Member, S., Cox, C. J., Lendaris, G. G., Fellow, L. & Saeks, R. (2002), ‘Adaptive Dynamic Programming’, IEEE Transactions on Systems, Man, and Cybernetics - Part C Applications and Reviews
2002
Earlier work this paper cites.
Camacho, E. & Bordons, C. (2003), Model Predictive Control
2003
Cited alongside, same era.
Spall, J. C. (2003), Introduction to Stochastic Search and Optimization: Estimation, simulation and control
2003
Cited alongside, same era.
J.A. Rossiter (2004), Model-Based Predictive Control
2004
Cited alongside, same era.
Kirk, D. E. (2004), Optimal Control Theory: An introduction
2004
Cited alongside, same era.
Si, J., Barto, A. G., Powell, W. B. & Wunsch, D. (2004), ‘Handbook of learning and approximate dynamic programming’, Wiley-IEEE Press
2004
Cited alongside, same era.
Chang, H. S., Fu, M. C., Hu, J. & Marcus, S. I. (2005), ‘An Adaptive Sampling Algorithm for Solving Markov Decision Processes’, Operations Research
Lewis, F. L., Vrabie, D. & Syrmos, V. L. (2012), Optimal Control
2012
Later among the works it cites.
Powell, W. B. & Ryzhov, I. O. (2012), Optimal Learning
2012
Later among the works it cites.
Rakovic, S. V., Kouvaritakis, B., Cannon, M., Panos, C. & Findeisen, R. (2012), ‘Parameterized tube model predictive control’, IEEE Transactions on Automatic Control
2012
Later among the works it cites.
Maxwell, M. S., Henderson, S. G. & Topaloglu, H. (2013), ‘Tuning approximate dynamic programming policies for ambulance redeployment via direct search’, Stochastic Systems
2013
Later among the works it cites.
Jiang, D. R., Pham, T. V., Powell, W. B., Salas, D. F. & Scott, W. R. (2014), A comparison of approximate dynamic programming techniques on benchmark energy storage problems: Does anything work?, in
2014
Later among the works it cites.
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2005
Cited alongside, same era.
Puterman, M. L. (2005), Markov Decision Processes
2005
Cited alongside, same era.
Coulom, R. (2007), Efficient selectivity and backup operators in Monte-Carlo tree search, in
2007
Cited alongside, same era.
Powell, W. B. & Frazier, P. I. (2008), ‘Optimal Learning’, TutORials in Operations Research
2008
Cited alongside, same era.
Lewis, F. L. & Vrabie, D. (2009), ‘Reinforcement Learning and Adaptive Dynamic Programming for Feedback Control’, IEEE Circuits And Systems Magazine
2009
Cited alongside, same era.
Simão, H., Day, J., George, A. P., Gifford, T., Nienow, J. & Powell, W. B. (2009), ‘An approximate dynamic programming algorithm for large-scale fleet management: A case application’, Transportation Science
2009
Cited alongside, same era.
Cinlar, E. (2011), Probability and Stochastics
2011
Cited alongside, same era.
Nisio, M. (2014), Stochastic Control Theory: Dynamic Programming Principle
2014
Later among the works it cites.
Powell, W. B. (2014), ‘Clearing the Jungle of Stochastic Optimization’, Bridging Data and Decisions
2015
Later among the works it cites.
Bouzaiene-Ayari, B., Cheng, C., Das, S., Fiorillo, R. & Powell, W. B. (2016), ‘From single commodity to multiattribute models for locomotive optimization: A comparison of optimal integer programming and approximate dynamic programming’, Transportation Science
2016
Later among the works it cites.
Powell, W. B. & Meisel, S. (2016), ‘Tutorial on Stochastic Optimization in Energy - Part II: An Energy Storage Illustration’, IEEE Transactions on Power Systems
2016
Later among the works it cites.
Fu, M. C. (2017), Markov Decision Processes, AlphaGo, and Monte Carlo Tree Search: Back to the Future, in
2017
Later among the works it cites.
Sutton, R. S. & Barto, A. G. (2018), Reinforcement Learning: An Introduction
2018
Later among the works it cites.
Lazaric, A. (2019), Introduction to Reinforcement Learning, in
2019
Closest in time.
Powell, W. B. (2019), ‘A unified framework for stochastic optimization’, European Journal of Operational Research
2019
Closest in time.
Recht, B. (2019), ‘A Tour of Reinforcement Learning: The View from Continuous Control’, Annual Review of Control, Robotics, and Autonomous Systems
2019
Closest in time.
Sethi, S. P. (2019), Optimal Control Theory: Applications to Management Science and Economics
2019
Closest in time.
Powell, W. B. (2020), Reinforcement Learning and Stochastic Optimization: A unified framework for sequential decisions
2020
Closest in time.