Deep Learning Approximation for Stochastic Control Problems
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Summary
This work develops a deep learning approach that directly solves high-dimensional stochastic control problems based on Monte-Carlo sampling and approximate the time-dependent controls as feedforward neural networks and stack these networks together through model dynamics.
- Type
- preprint
- Published
- 2016-11-02
- Cited by
- 225
- References
- 21
- Access
- Open access
- OpenAlex
- https://openalex.org/W2551937961
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:16386889
Keywords
Control (management), Mathematical economics, Computer science, Artificial intelligence, Economics
References
- An Approximate Dynamic Programming Algorithm for Monotone Value Functions
- Optimal control of execution costs for portfolios
- Optimal control of execution costs
- Approximate dynamic programming: solving the curses of dimensionality
- Learning Deep Architectures for AI
- Gradient-based learning applied to document recognition
- Human-level control through deep reinforcement learning
- ImageNet classification with deep convolutional neural networks
- Mastering the game of Go with deep neural networks and tree search
- Reinforcement learning
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- Reinforcement learning
- Deep Learning
- Benchmarking Deep Reinforcement Learning for Continuous Control
- Continuous control with deep reinforcement learning
- Adam: A Method for Stochastic Optimization
- High-Dimensional Continuous Control Using Generalized Advantage Estimation
- Trust Region Policy Optimization
- Benchmarking a Scalable Approximate Dynamic Programming Algorithm for Stochastic Control of Multidimensional Energy Storage Problems
Cited by
- Deep Learning-Based Numerical Methods for High-Dimensional Parabolic Partial Differential Equations and Backward Stochastic Differential Equations
- Machine Learning Approximation Algorithms for High-Dimensional Fully Nonlinear Partial Differential Equations and Second-order Backward Stochastic Differential Equations
- Backward Approximate Dynamic Programming with Hidden Semi-Markov Stochastic Models in Energy Storage Optimization
- Asymptotic Expansion as Prior Knowledge in Deep Learning Method for High dimensional BSDEs
- Dynamic dispatching for re-entrant production lines — A deep learning approach
- Simulation methods for stochastic storage problems: a statistical learning perspective
- Deep Optimal Stopping
- A Deep Neural Network Surrogate for High-Dimensional Random Partial Differential Equations
- Predicting Solution Summaries to Integer Linear Programs under Imperfect Information with Machine Learning
- Machine Learning for Semi Linear PDEs
- A Mathematical Model for Vineyard Replacement with Nonlinear Binary Control Optimization
- Monge-Ampère Flow for Generative Modeling
- Deep neural networks algorithms for stochastic control problems on finite horizon, part I: convergence analysis
- Stochastic Optimal Control Scheme for Operation Cost Management in Energy Internet
- The Dominium Mundi Game and the Case for Artificial Intelligence in Economics and the Law
- Approximate stochastic control based on deep learning and forward backward stochastic differential equations
- A Lane-Change Path Planner and its application with a monocular camera
- Deep Learning Based Online Power Control for Large Energy Harvesting Networks
- Rectified deep neural networks overcome the curse of dimensionality for nonsmooth value functions in zero-sum games of nonlinear stiff systems
- A Neural Network-Based Policy Iteration Algorithm with Global H2[12pt]minimal amsmath wasysym amsfonts amssymb amsbsy mathrsfs upgreek -69pt documentH^2\
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