A New Method for Computing Asymptotic Results in Optimal Stopping Problems

A New Optimal Solution Concept for Fuzzy Optimal Control Problems

In this paper, we propose the new concept of optimal solution for fuzzy variational problems based on the possibility and necessity measures. Inspired by the well–known embedding theorem, we can transform the fuzzy variational problem into a bi–objective variational problem. Then the optimal solutions of fuzzy variational problem can be obtained by solving its corresponding biobjective variatio...

Optimal Stopping Problems

In the last lecture, we have analyzed the behavior of TD(λ) for approximating the cost­to­go function in autonomous systems. Recall that much of the analysis was based on the idea of sampling states according to their stationary distribution. This was done either explicitly, as was assumed in approximate value iteration, or implicitly through the simulation or observation of system trajectories...

A Method for Solving Optimal Control Problems Using Genetic Programming

This paper deals with a novel method for solving optimal control problems based on genetic programming. This approach produces some trial solutions and seeks the best of them. If the solution cannot be expressed in a closed analytical form then our method produces an approximation with a controlled level of accuracy. Using numerical examples, we will demonstrate how to use the results.

On Asymptotic Behavior of Stopping Time Problems

A New Learning Algorithm for Optimal Stopping

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.