Scenarios and policy aggregation in optimization under uncertainty
Mathematics of Operations Research
Barycentric scenario trees in convex multistage stochastic programming
Mathematical Programming: Series A and B
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Generating Scenario Trees for Multistage Decision Problems
Management Science
Apprenticeship learning via inverse reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Epi-Convergent Discretizations of Multistage Stochastic Programs
Mathematics of Operations Research
Applications of Stochastic Programming (Mps-Siam Series on Optimization) (Mps-Saimseries on Optimization)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Neurocomputing
Confidence level solutions for stochastic programming
Automatica (Journal of IFAC)
Step decision rules for multistage stochastic programming: A heuristic approach
Automatica (Journal of IFAC)
Learning for control from multiple demonstrations
Proceedings of the 25th international conference on Machine learning
Apprenticeship learning using linear programming
Proceedings of the 25th international conference on Machine learning
Epi-convergent discretizations of multistage stochastic programs via integration quadratures
Mathematical Programming: Series A and B - Nonlinear convex optimization and variational inequalities
Scenario tree modeling for multistage stochastic programs
Mathematical Programming: Series A and B
Bounds for multistage stochastic programs using supervised learning strategies
SAGA'09 Proceedings of the 5th international conference on Stochastic algorithms: foundations and applications
Algorithms for Reinforcement Learning
Algorithms for Reinforcement Learning
Reinforcement Learning and Dynamic Programming Using Function Approximators
Reinforcement Learning and Dynamic Programming Using Function Approximators
Monte Carlo bounding techniques for determining solution quality in stochastic programs
Operations Research Letters
| Hi-index | 0.00 |
In the context of multistage stochastic optimization problems, we propose a hybrid strategy for generalizing to nonlinear decision rules, using machine learning, a finite data set of constrained vector-valued recourse decisions optimized using scenario-tree techniques from multistage stochastic programming. The decision rules are based on a statistical model inferred from a given scenario-tree solution and are selected by out-of-sample simulation given the true problem. Because the learned rules depend on the given scenario tree, we repeat the procedure for a large number of randomly generated scenario trees and then select the best solution policy found for the true problem. The scheme leads to an ex post selection of the scenario tree itself. Numerical tests evaluate the dependence of the approach on the machine learning aspects and show cases where one can obtain near-optimal solutions, starting with a “weak” scenario-tree generator that randomizes the branching structure of the trees.