Results for “EV aggregate model”

NASA NTRS1972

Models for the interaction of rarefied gas with a surface

Various theoretical models are discussed for the interaction of gaseous atoms with solids at energies of approximately 10 eV. The surface is modeled by a one- and three-dimensional aggregate of atoms with different initial conditions and lattice parameters. In calculating the accommodation coefficients, the direct integration of the equations of motion of gaseous molecules and atoms in the crystal lattice of the solid is used. An analysis of the advantages and the drawbacks of the models, as well as the possibilities of the analytical solutions in a one-dimensional case, is given. The dependence of the energy and the normal and the tangential momenta accommodation coefficients on the collision parameters is obtained.

Pyarnpuu, A. A.↗

NASA NTRS1999

Reinforcement Learning in a Nonstationary Environment: The El Farol Problem

This paper examines the performance of simple learning rules in a complex adaptive system based on a coordination problem modeled on the El Farol problem. The key features of the El Farol problem are that it typically involves a medium number of agents and that agents' pay-off functions have a discontinuous response to increased congestion. First we consider a single adaptive agent facing a stationary environment. We demonstrate that the simple learning rules proposed by Roth and Er'ev can be extremely sensitive to small changes in the initial conditions and that events early in a simulation can affect the performance of the rule over a relatively long time horizon. In contrast, a reinforcement learning rule based on standard practice in the computer science literature converges rapidly and robustly. The situation is reversed when multiple adaptive agents interact: the RE algorithms often converge rapidly to a stable average aggregate attendance despite the slow and erratic behavior of individual learners, while the CS based learners frequently over-attend in the early and intermediate terms. The symmetric mixed strategy equilibria is unstable: all three learning rules ultimately tend towards pure strategies or stabilize in the medium term at non-equilibrium probabilities of attendance. The brittleness of the algorithms in different contexts emphasize the importance of thorough and thoughtful examination of simulation-based results.

Bell, Ann Maria↗