Convergent activation dynamics in continuous time networks
Neural Networks
Stochastic approximation with two time scales
Systems & Control Letters
The O.D. E. Method for Convergence of Stochastic Approximation and Reinforcement Learning
SIAM Journal on Control and Optimization
Optimization by Vector Space Methods
Optimization by Vector Space Methods
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Linear Programming Formulation for Optimal Stopping Problems
SIAM Journal on Control and Optimization
SIAM Journal on Control and Optimization
The Linear Programming Approach to Approximate Dynamic Programming
Operations Research
Pricing American Options: A Duality Approach
Operations Research
Interpolation-based Q-learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
On Constraint Sampling in the Linear Programming Approach to Approximate Dynamic Programming
Mathematics of Operations Research
A Generalized Kalman Filter for Fixed Point Approximation and Efficient Temporal-Difference Learning
Discrete Event Dynamic Systems
Function-approximation-based importance sampling for pricing American options
WSC '04 Proceedings of the 36th conference on Winter simulation
Regression methods for pricing complex American-style options
IEEE Transactions on Neural Networks
Pathwise Optimization for Optimal Stopping Problems
Management Science
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A linear programming formulation of the optimal stopping problem for Markov decision processes is approximated using linear function approximation. Using this formulation, a reinforcement learning scheme based on a primal-dual method and incorporating a sampling device called `split sampling' is proposed and analyzed. An illustrative example from option pricing is also included.