Markov Decision Processes and Reinforcement Learning · Motivation earning policies through reinforcement Q values

How Might a mouse search a Maze for Cheese? heese · State Space Search? As a Constraint Satisfaction Problem? Goal-directed Planning As a rule or production System? What is missing? Ideas in this lecture Objective is to accumulate rewards rather than goal states Task is to generate policies for how to act in all situations rather than a plan for a single starting situation

Robot Localization using SIR Take sample set X+_11 ll. iterate For each sample xt-1 1) Sample from p(X,1 X+1, a) 2) Attach importance weights

Probabilistic model Integrate over al!p(x1 Bel(x,)=ap(=;|x,)p(x1a1,=12a1-2,=0) ap(E, x )p(x, Ix, 1,a, p(|x,)p(x,1x1,a1)p(x1)d

Assignment problem Persons and tasks connected by links Objective: Minimize total task time minutes Carburetor Manny 4 minutes Valves

Courtesy or Eric Feron and Sommer Gentry. Used with permission. Introduction to Linear Programming Eric Feron (updated Sommer Gentry)

Integer Programs LP: Maximize 3x1+ 4x2 IP: Maximize 3x1+ 4x2 Subiect to: Subject te

Robot localization 3 canonical problems Position Tracking Global Localization Kidnapped robot problem onot the mapping problem. We always assume we have a map To start without a map or model is the Simultaneous Localization and Mapping problem, or SLAM. also known as concurrent Mapping and Localization

WORLD observations Plant actions P(S sense act Diagnostic Agent Monitors diagnoses AGENT Repairs avoids Probes and tests

Roadmap path planning Brian C. williams 16410-13 October 27th. 2003 Assignment · Reading: Path Planning: AIMA Ch 25.4 Homework Online problem set #7 due Monday, November 3rd