Public

Enables/disables print commands to the given debug code

toggleCode

A print command for the given debug code. If that debug code is set to false,
then the print will no

A print line command for the given debug code. If that debug code is set to
false, then the print wi

A universal print whose behavior is determined by the universalPrint field

SimpleDateFormat

Finding current android device location

A Window object is a top-level window with no borders and no menubar. The
default layout for a windo

java.awt.Window

Vector is an implementation of List, backed by an array and synchronized. All
optional operations in

java.util.Vector

Patterns are compiled regular expressions. In many cases, convenience methods
such as String#matches

java.util.regex.Pattern

The Modifier class provides static methods and constants to decode class and
member access modifiers

javassist.Modifier

javax.swing.JCheckBox

Basic implementation of javax.sql.DataSource that is configured via JavaBeans
properties. This is no

org.apache.commons.dbcp.BasicDataSource

compareTo

scheduleAtFixedRate

getSystemService

/**
	 * A print line command for the given debug code. If that debug code is set to false, then the print will not occur.
	 * @param c the debug code under which printing should be performed
	 * @param s the string to print
	 */

/**
	 * Implements the "projection method" for calculating a policy-tilde with a
	 * feature expectation within epsilon of an expert's feature expectation.
	 * As described in:
	 * Abbeel, Peter and Ng, Andrew. "Apprenticeship Learning via Inverse Reinforcement Learning"
	 * 
	 * Takes in an expert's samples in some domain given some features, and returns a list of
	 * policies that can be evaluated algorithmically or manually.
	 * 
	 * Note: I've stored policy histories AND feature expectation histories
	 *
	 * @param request contains all of the IRL request informaiton.
	 * @return the policy that mimics the expert
	 */

//Max steps that the apprentice will have to learn

//(0) set up policy array; exper feature expectation

// (1b) Set up initial Feature Expectation based on policy

// (2) Compute weights and score using projection method

//THIS IS THE KEY DIFFERENCE BETWEEN THE MAXIMUM MARGIN METHOD AND THE PROJECTION METHOD

//On the first iteration, the projection is just set as the current feature expectation

//don't forget to set the old projection to the new one!

// (4b) Compute optimal policy for pi^(i) give R

/**
	 * Method which implements high level algorithm provided section 3 of
	 * Abbeel, Peter and Ng, Andrew. "Apprenticeship Learning via Inverse Reinforcement Learning"
	 * @param request contains all of the IRL request informaiton.
	 * @return the computed {@link Policy}
	 */

// Need to evaluate policies with trajectory lengths equal to that of the demonstrated episodes

// (2) Compute t^(i) = max_w min_j (wT (uE - u^(j)))

How to use cmethodin burlap.debugtools.DPrint

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