PPT-1 Monte-Carlo Planning: Introduction and Bandit Basics

Alan Fern 2 Large Worlds We have considered basic modelbased planning algorithms Modelbased planning assumes MDP model is available Methods we learned so far are at least polytime in the number of states and actions. X is a random vector in is a function from to and E Note that could represent the values of a stochastic process at di64256erent points in time For example might be the price of a particular stock at time and might be given by so then is the expe Steven . Gollmer. Cedarville University. Meet and Greet Game. Are there people here who share the same birthday?. Most births occur in September & October. October 5. th. is the most common birthday. Basic Principles and Recent Progress. Most slides by. Alan . Fern. EECS, Oregon . State . University. A few from me, Dan Klein, Luke . Zettlmoyer. , etc . Dan Weld – UW CSE 573. October 2012. An introduction to Monte Carlo techniques. ENGS168. Ashley Laughney. November 13. th. , 2009. Overview of Lecture. Introduction to the Monte Carlo Technique. Stochastic modeling. Applications (with a focus on Radiation Transport). MWERA 2012. Emily A. Price, MS. Marsha Lewis, MPA . Dr. . Gordon P. Brooks. Objectives and/or Goals. Three main parts. Data generation in R. Basic Monte Carlo programming (e.g. loops). Running simulations (e.g., investigating Type I errors). (Monaco). Monte Carlo Timeline. 10 June 1215. Monaco is taken by the Genoese. 1489. The King of France, Charles VIII, and the Duke of Savoy recognize the sovereignty of Monaco . 1512. Louis XII, King of France, recognizes the independence of Monaco. SIMULATION. Simulation . of a process . – the examination . of any emulating process simpler than that under consideration. .. Examples:. System’s Simulation such as simulation of engineering systems, large organizational systems, and governmental systems. Simple Monte Carlo . Integration. Suppose . that we pick N random points, . uniformly . distributed in a . multidimensional volume . V . Call . them x. 0. ,… . ; . x. N-1. . Then the basic theorem of Monte . Decision Making. Copyright © 2004 David M. Hassenzahl. What is Monte Carlo Analysis?. It is a tool for combining . distributions. , and thereby propagating more than just summary statistics. It uses . Monte . Carlo Simulation. Monte Carlo simulations in PSpice can be run as either:. a worst case analysis where the maximum deviation from the nominal values of each component are used in the calculations. Alan Fern . 2. Monte-Carlo Planning. Often a . simulator. of a planning domain is available. or can be learned from data. 2. Fire & Emergency Response. Conservation Planning. 3. Large Worlds: Monte-Carlo Approach. 19: High Quality Rendering . Ravi . Ramamoorthi. http:/. /viscomp.ucsd.edu/classes/cse167/wi17. Summary. This is the final lecture of CSE 167. Good luck on HW 4, written assignment. Please consider CSE 163 (mine), CSE 168 spring. José A. Ramos Méndez, PhD.. University of California San Francisco. Outline. Introduction. Basics of Monte Carlo method. Statistical Uncertainty. Improving Efficiency Techniques. 11/2/15. FCFM-BUAP, Puebla, Pue.. Rustom D. Sutaria – Avia Intelligence 2016 , Dubai Introduction Risk analysis is an increasing part of every decision we make where aircraft maintenance planning & reliability are concerned . A