Pages that link to "Item:Q5151934"
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The following pages link to Deep Neural Networks Algorithms for Stochastic Control Problems on Finite Horizon: Convergence Analysis (Q5151934):
Displaying 42 items.
- Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space (Q825596) (← links)
- A neural network-based policy iteration algorithm with global \(H^2\)-superlinear convergence for stochastic games on domains (Q2031059) (← links)
- A deep learning model for gas storage optimization (Q2064630) (← links)
- Polynomial affine approach to HARA utility maximization with applications to OrnsteinUhlenbeck \(4/2\) models. (Q2073105) (← links)
- Deep combinatorial optimisation for optimal stopping time problems: application to swing options pricing. (Q2094859) (← links)
- Using stochastic programming to train neural network approximation of nonlinear MPC laws (Q2097845) (← links)
- Automatic model training under restrictive time constraints (Q2108929) (← links)
- Robust utility maximization under model uncertainty via a penalization approach (Q2120592) (← links)
- Deep learning for constrained utility maximisation (Q2152236) (← links)
- A mean-field optimal control formulation of deep learning (Q2319864) (← links)
- Linear-quadratic stochastic delayed control and deep learning resolution (Q2664898) (← links)
- Deep neural networks algorithms for stochastic control problems on finite horizon: numerical applications (Q2671220) (← links)
- Solving stochastic optimal control problem via stochastic maximum principle with deep learning method (Q2676795) (← links)
- Numerical resolution of McKean-Vlasov FBSDEs using neural networks (Q2684929) (← links)
- Adaptive Deep Learning for High-Dimensional Hamilton--Jacobi--Bellman Equations (Q4997364) (← links)
- Semiglobal optimal feedback stabilization of autonomous systems via deep neural network approximation (Q4999517) (← links)
- Approximation Error Analysis of Some Deep Backward Schemes for Nonlinear PDEs (Q5021399) (← links)
- An Algorithm to Construct Subsolutions of Convex Optimal Control Problems (Q5039275) (← links)
- Discrete-Time Portfolio Optimization under Maximum Drawdown Constraint with Partial Information and Deep Learning Resolution (Q5050082) (← links)
- (Q5053195) (← links)
- Error Estimates for a Tree Structure Algorithm Solving Finite Horizon Control Problems (Q5056666) (← links)
- SympOCnet: Solving Optimal Control Problems with Applications to High-Dimensional Multiagent Path Planning Problems (Q5058288) (← links)
- Full error analysis for the training of deep neural networks (Q5083408) (← links)
- Deep learning for ranking response surfaces with applications to optimal stopping problems (Q5139253) (← links)
- Optimal Market Making with Persistent Order Flow (Q5162846) (← links)
- A Machine Learning Approach to Adaptive Robust Utility Maximization and Hedging (Q5162848) (← links)
- Value-Gradient Based Formulation of Optimal Control Problem and Machine Learning Algorithm (Q6040292) (← links)
- Finite Horizon Impulse control of Stochastic Functional Differential Equations (Q6042798) (← links)
- Overcoming the timescale barrier in molecular dynamics: Transfer operators, variational principles and machine learning (Q6047503) (← links)
- Deep empirical risk minimization in finance: Looking into the future (Q6054448) (← links)
- Event-triggered fault-tolerant control for input-constrained nonlinear systems with mismatched disturbances via adaptive dynamic programming (Q6057954) (← links)
- Strong stationarity for optimal control problems with non-smooth integral equation constraints: application to a continuous DNN (Q6058518) (← links)
- Three ways to solve partial differential equations with neural networks — A review (Q6068232) (← links)
- Optimal polynomial feedback laws for finite horizon control problems (Q6072899) (← links)
- Neural networks for first order HJB equations and application to front propagation with obstacle terms (Q6087416) (← links)
- Deep reinforcement learning in finite-horizon to explore the most probable transition pathway (Q6118140) (← links)
- A Normalizing Field Flow Induced Two-Stage Stochastic Homogenization Method for Random Composite Materials (Q6142999) (← links)
- Beating a Benchmark: Dynamic Programming May Not Be the Right Numerical Approach (Q6159077) (← links)
- Optimal liquidation through a limit order book: a neural network and simulation approach (Q6164829) (← links)
- Across-time risk-aware strategies for outperforming a benchmark (Q6555163) (← links)
- Lax-Oleinik-type formulas and efficient algorithms for certain high-dimensional optimal control problems (Q6575313) (← links)
- Recent developments in machine learning methods for stochastic control and games (Q6615618) (← links)
