Glosarium Matematika

273 p.; 24 cm

Following Newton direction in Policy Gradient with parameter exploration

This paper investigates the use of second-order methods to solve Markov Decision Processes (MDPs). Despite the popularity of second-order methods in optimization literature, so far little attention has been paid to the extension of such techniques to face sequential decision problems. Here we provide a model-free Reinforcement Learning method that estimates the Newton direction by sampling directly in the parameter space. In order to compute the Newton direction we provide the formulation of the Hessian of the expected return, a technique for variance reduction in the sample-based estimation and a finite sample analysis in the case of Normal distribution. Beside discussing the theoretical properties, we empirically evaluate the method on an instructional linear-quadratic regulator and on a complex dynamical quadrotor system

Data validation and reliability calculations in digital protection systems

Imperial Users onl

Pathways to randomness in the economy: Emergent nonlinearity and chaos in economics and finance

This paper: (1) Gives a general argument why research on nonlinear science in general and chaos in particular is important in economics and finance. (2) Puts forth two definitions of stochastic nonlinearity (IID-Linearity and MDS-Linearity) for nonlinear time series analysis and argues for their usefulness as organizing concepts not only for discussion of nonlinearity testinf but also for building a new class of structural asset pricing models. (3) Shows how to use ideas from interacting particle systems theory to build structural asset pricing models that turn IID-Linear or MDS-Linear earnings processes into non MDS-Linear equilibrium returns processes.