Sensitivity-analysis in discounted Markovian decision problems

Non-discounted Denumerable Markovian Decision Models

Non-Markovian Policies in Sequential Decision Problems

In this article we prove the validity of the Dellman Optimality Equa­ tion a.nd related results for sequential decision problems with a general recursive structure. The characteristic feature of our approach is that also non-Markovian policies are taken into account. The theory is moti­ vated by some experiments with a learning robot.

Sensitivity Analysis of Markovian Models

Sensitivity analysis of Markovian models amounts to computing the constants in polynomial functions of a parameter under study. To handle the computational complexity involved, we propose a method for approximate sensitivity analysis of such models. We show that theoretical properties allow us to reason for the present time using just few observations from the past with small loss in accuracy. ...

SENSITIVITY ANALYSIS IN LINEAR-PLUS-LINEAR FRACTIONAL PROGRAMMING PROBLEMS

In this paper, we study the classical sensitivity analysis when the right - hand – side vector, and the coefficients of the objective function are allowed to vary. 

Efficient approximate planning in continuous space Markovian Decision Problems

MDPs provide a clean and simple, yet fairly rich framework for studying various aspects of intelligence, such as planning. A well-known practical limitation of planning in MDPs is called the curse of dimensionality [1], referring to the exponential rise in the resources required to compute (even approximate) solutions to an MDP as the size of the MDP (the number of state variables) increases. F...