A simulation-based approach to stochastic dynamic programming
From MaRDI portal
Publication:2863720
DOI10.1002/asmb.896zbMath1277.90144OpenAlexW2163388594MaRDI QIDQ2863720
Nicholas G. Polson, Morten Heine B. Sørensen
Publication date: 3 December 2013
Published in: Applied Stochastic Models in Business and Industry (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1002/asmb.896
dynamic programmingsequential Monte Carlostochastic simulationBellman\(\mathcal Q\)-learningsimulatedannealing
Related Items (2)
Advances in Bayesian decision making in reliability ⋮ Augmented Markov Chain Monte Carlo Simulation for Two-Stage Stochastic Programs with Recourse
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Optimization by Simulated Annealing
- Asynchronous stochastic approximation and Q-learning
- Convergence results for single-step on-policy reinforcement-learning algorithms
- \({\mathcal Q}\)-learning
- The convergence of \(TD(\lambda)\) for general \(\lambda\)
- A survey of algorithmic methods for partially observed Markov decision processes
- 10.1162/153244303768966102
- Information Relaxations and Duality in Stochastic Dynamic Programs
- Policy Improvement and the Newton-Raphson Algorithm
- Quadrature-Based Methods for Obtaining Approximate Solutions to Nonlinear Asset Pricing Models
- Approximations of Dynamic Programs, I
- Iterated Random Functions
- On the Convergence of Stochastic Iterative Dynamic Programming Algorithms
- Using Randomization to Break the Curse of Dimensionality
- 10.1162/1532443041827907
- Sequential Monte Carlo Techniques for Solving Non-Linear Systems
- A Retrospective and Prospective Survey of the Monte Carlo Method
- Letter to the Editor—A Monte Carlo Method for the Approximate Solution of Certain Types of Constrained Optimization Problems
- Polynomial Approximation--A New Computational Technique in Dynamic Programming: Allocation Processes
- Optimal Bayesian Design by Inhomogeneous Markov Chain Simulation
- Bayesian-Optimal Design via Interacting Particle Systems
- A Stochastic Approximation Method
This page was built for publication: A simulation-based approach to stochastic dynamic programming
