Open Access. Powered by Scholars. Published by Universities.®
Operations Research, Systems Engineering and Industrial Engineering Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
Articles 1 - 2 of 2
Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Higher-Order Effects In Biaxial Flexure Of Gfrp I-Section Beams, Zia Razzaq, Faridoon Z. Razzaq
Higher-Order Effects In Biaxial Flexure Of Gfrp I-Section Beams, Zia Razzaq, Faridoon Z. Razzaq
Civil & Environmental Engineering Faculty Publications
A theoretical study of Glass Fiber Reinforced Polymer (GFRP) beams subjected to biaxial bending moments is presented with a focus on the influence of higher-order effects on maximum normal stresses. It is shown that the biaxial bending type of loading causes a dramatic increase in the maximum normal stress for a GFRP beam when induced torsional effects are included. The study demonstrates that the traditional first-order theory can grossly underestimate the maximum normal stress in a GFRP beam. Based on the numerical results presented using a higher-order theory which also accounts for induced warping normal stresses, the maximum normal stress …
Deep Reinforcement Learning For Approximate Policy Iteration: Convergence Analysis And A Post-Earthquake Disaster Response Case Study, Abhijit Gosavi, L. (Lesley) H. Sneed, L. A. Spearing
Deep Reinforcement Learning For Approximate Policy Iteration: Convergence Analysis And A Post-Earthquake Disaster Response Case Study, Abhijit Gosavi, L. (Lesley) H. Sneed, L. A. Spearing
Engineering Management and Systems Engineering Faculty Research & Creative Works
Approximate Policy Iteration (API) is a Class of Reinforcement Learning (RL) Algorithms that Seek to Solve the Long-Run Discounted Reward Markov Decision Process (MDP), Via the Policy Iteration Paradigm, Without Learning the Transition Model in the Underlying Bellman Equation. Unfortunately, These Algorithms Suffer from a Defect Known as Chattering in Which the Solution (Policy) Delivered in Each Iteration of the Algorithm Oscillates between Improved and Worsened Policies, Leading to Sub-Optimal Behavior. Two Causes for This that Have Been Traced to the Crucial Policy Improvement Step Are: (I) the Inaccuracies in the Policy Improvement Function and (Ii) the Exploration/exploitation Tradeoff Integral …