ReinforcementLearningAgentTest.java

package aima.test.core.unit.learning.reinforcement.agent;

import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

import org.junit.Assert;

import aima.core.environment.cellworld.Cell;
import aima.core.environment.cellworld.CellWorld;
import aima.core.environment.cellworld.CellWorldAction;
import aima.core.environment.cellworld.CellWorldFactory;
import aima.core.learning.reinforcement.agent.ReinforcementAgent;
import aima.core.learning.reinforcement.example.CellWorldEnvironment;
import aima.core.probability.example.MDPFactory;
import aima.core.util.JavaRandomizer;

public abstract class ReinforcementLearningAgentTest {

	public static void test_RMSeiu_for_1_1(
			ReinforcementAgent<Cell<Double>, CellWorldAction> reinforcementAgent,
			int numRuns, int numTrialsPerRun,
			double expectedErrorLessThan) {
		CellWorld<Double> cw = CellWorldFactory.createCellWorldForFig17_1();
		CellWorldEnvironment cwe = new CellWorldEnvironment(
				cw.getCellAt(1, 1),
				cw.getCells(),
				MDPFactory.createTransitionProbabilityFunctionForFigure17_1(cw),
				new JavaRandomizer());

		cwe.addAgent(reinforcementAgent);

		Map<Integer, Map<Cell<Double>, Double>> runs = new HashMap<Integer, Map<Cell<Double>, Double>>();
		for (int r = 0; r < numRuns; r++) {
			reinforcementAgent.reset();
			cwe.executeTrials(numTrialsPerRun);
			runs.put(r, reinforcementAgent.getUtility());
		}
		
		// Calculate the Root Mean Square Error for utility of 1,1
		// for this trial# across all runs
		double xSsquared = 0;
		for (int r = 0; r < numRuns; r++) {
			Map<Cell<Double>, Double> u = runs.get(r);
			Double val1_1 = u.get(cw.getCellAt(1, 1));
			if (null == val1_1) {
				throw new IllegalStateException("U(1,1,) is not present: r="+r+", u="+u);
			}
			xSsquared += Math.pow(0.705 - val1_1, 2);
		}
		double rmse = Math.sqrt(xSsquared / runs.size());
		Assert.assertTrue(""+rmse+" is not < "+expectedErrorLessThan, rmse < expectedErrorLessThan);
	}
	
	public static void test_utility_learning_rates(
			ReinforcementAgent<Cell<Double>, CellWorldAction> reinforcementAgent,
			int numRuns, int numTrialsPerRun, int rmseTrialsToReport,
			int reportEveryN) {

		if (rmseTrialsToReport > (numTrialsPerRun / reportEveryN)) {
			throw new IllegalArgumentException(
					"Requesting to report too many RMSE trials, max allowed for args is "
							+ (numTrialsPerRun / reportEveryN));
		}

		CellWorld<Double> cw = CellWorldFactory.createCellWorldForFig17_1();
		CellWorldEnvironment cwe = new CellWorldEnvironment(
				cw.getCellAt(1, 1),
				cw.getCells(),
				MDPFactory.createTransitionProbabilityFunctionForFigure17_1(cw),
				new JavaRandomizer());

		cwe.addAgent(reinforcementAgent);

		Map<Integer, List<Map<Cell<Double>, Double>>> runs = new HashMap<Integer, List<Map<Cell<Double>, Double>>>();
		for (int r = 0; r < numRuns; r++) {
			reinforcementAgent.reset();
			List<Map<Cell<Double>, Double>> trials = new ArrayList<Map<Cell<Double>, Double>>();
			for (int t = 0; t < numTrialsPerRun; t++) {
				cwe.executeTrial();
				if (0 == t % reportEveryN) {
					Map<Cell<Double>, Double> u = reinforcementAgent.getUtility();
					if (null == u.get(cw.getCellAt(1, 1))) {
						throw new IllegalStateException("Bad Utility State Encountered: r="+r+", t="+t+", u="+u);
					}
					trials.add(u);
				}
			}
			runs.put(r, trials);
		}

		StringBuilder v4_3 = new StringBuilder();
		StringBuilder v3_3 = new StringBuilder();
		StringBuilder v1_3 = new StringBuilder();
		StringBuilder v1_1 = new StringBuilder();
		StringBuilder v3_2 = new StringBuilder();
		StringBuilder v2_1 = new StringBuilder();
		for (int t = 0; t < (numTrialsPerRun / reportEveryN); t++) {
			// Use the last run
			Map<Cell<Double>, Double> u = runs.get(numRuns - 1).get(t);
			v4_3.append((u.containsKey(cw.getCellAt(4, 3)) ? u.get(cw
					.getCellAt(4, 3)) : 0.0)
					+ "\t");
			v3_3.append((u.containsKey(cw.getCellAt(3, 3)) ? u.get(cw
					.getCellAt(3, 3)) : 0.0)
					+ "\t");
			v1_3.append((u.containsKey(cw.getCellAt(1, 3)) ? u.get(cw
					.getCellAt(1, 3)) : 0.0)
					+ "\t");
			v1_1.append((u.containsKey(cw.getCellAt(1, 1)) ? u.get(cw
					.getCellAt(1, 1)) : 0.0)
					+ "\t");
			v3_2.append((u.containsKey(cw.getCellAt(3, 2)) ? u.get(cw
					.getCellAt(3, 2)) : 0.0)
					+ "\t");
			v2_1.append((u.containsKey(cw.getCellAt(2, 1)) ? u.get(cw
					.getCellAt(2, 1)) : 0.0)
					+ "\t");
		}
		System.out.println("(4,3)" + "\t" + v4_3);
		System.out.println("(3,3)" + "\t" + v3_3);
		System.out.println("(1,3)" + "\t" + v1_3);
		System.out.println("(1,1)" + "\t" + v1_1);
		System.out.println("(3,2)" + "\t" + v3_2);
		System.out.println("(2,1)" + "\t" + v2_1);

		StringBuilder rmseValues = new StringBuilder();
		for (int t = 0; t < rmseTrialsToReport; t++) {
			// Calculate the Root Mean Square Error for utility of 1,1
			// for this trial# across all runs
			double xSsquared = 0;
			for (int r = 0; r < numRuns; r++) {
				Map<Cell<Double>, Double> u = runs.get(r).get(t);
				Double val1_1 = u.get(cw.getCellAt(1, 1));
				if (null == val1_1) {
					throw new IllegalStateException("U(1,1,) is not present: r="+r+", t="+t+", runs.size="+runs.size()+", runs(r).size()="+runs.get(r).size()+", u="+u);
				}
				xSsquared += Math.pow(0.705 - val1_1, 2);
			}
			double rmse = Math.sqrt(xSsquared / runs.size());
			rmseValues.append(rmse);
			rmseValues.append("\t");
		}
		System.out.println("RMSeiu" + "\t" + rmseValues);
	}
}