PPT-Monte Carlo Tree Search & Stochastic Games

Outline I Monte Carlo tree search MCTS Figuresimages are from the textbook site or by the instructor II Stochastic games I One Iteration of MCTS Step 1 Selection Root state just after the. 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 Some of the fastest known algorithms for certain tasks rely on chance. Stochastic/Randomized Algorithms. Two common variations. Monte Carlo. Las Vegas. We have already encountered some of both in this class. 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. Alan Fern . 2. Introduction. Rollout does not guarantee optimality or near optimality. It only guarantees policy . improvement (under certain conditions). Theoretical Question:. Can . we develop Monte-Carlo methods that give us near optimal policies?. 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). Monte Carlo Tree Search. Minimax. search fails for games with deep trees, large branching factor, and no simple heuristics. Go: branching factor . 361 (19x19 board). Monte Carlo Tree Search. Instead . Alan Fern . 2. Introduction. Rollout does not guarantee optimality or near optimality. It only guarantees policy . improvement (under certain conditions). Theoretical Question:. Can . we develop Monte-Carlo methods that give us near optimal policies?. (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. Stochastic Games, Stochastic Search, and Learned Evaluation Functions. Slides by Svetlana Lazebnik, 9/2016. Modified by Mark Hasegawa-Johnson, 9/2017. Types of game environments. Deterministic. Stochastic. 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. 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