RL4J - Added a unit test to help refac QLearningDiscrete.trainStep()

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/learning/sync/qlearning/QLearning.java

@@ -45,8 +45,11 @@
 public abstract class QLearning<O extends Encodable, A, AS extends ActionSpace<A>>
                 extends SyncLearning<O, A, AS, IDQN> {
 
-    @Getter
-    final private IExpReplay<A> expReplay;
+    // FIXME Changed for refac
+    // @Getter
+    // final private IExpReplay<A> expReplay;
+    @Getter @Setter
+    private IExpReplay<A> expReplay;
 
     public QLearning(QLConfiguration conf) {
         super(conf);

rl4j/rl4j-core/src/test/java/org/deeplearning4j/rl4j/learning/async/AsyncThreadTest.java

@@ -1,17 +1,16 @@
 package org.deeplearning4j.rl4j.learning.async;
 
-import org.deeplearning4j.gym.StepReply;
 import org.deeplearning4j.nn.api.NeuralNetwork;
 import org.deeplearning4j.nn.gradient.Gradient;
 import org.deeplearning4j.rl4j.learning.IHistoryProcessor;
 import org.deeplearning4j.rl4j.mdp.MDP;
 import org.deeplearning4j.rl4j.network.NeuralNet;
 import org.deeplearning4j.rl4j.policy.Policy;
-import org.deeplearning4j.rl4j.space.DiscreteSpace;
 import org.deeplearning4j.rl4j.space.Encodable;
-import org.deeplearning4j.rl4j.space.ObservationSpace;
 import org.deeplearning4j.rl4j.support.MockDataManager;
 import org.deeplearning4j.rl4j.support.MockHistoryProcessor;
+import org.deeplearning4j.rl4j.support.MockMDP;
+import org.deeplearning4j.rl4j.support.MockObservationSpace;
 import org.deeplearning4j.rl4j.util.IDataManager;
 import org.junit.Test;
 import org.nd4j.linalg.api.ndarray.INDArray;
@@ -93,7 +92,7 @@ public void refac_withHistoryProcessor_isSaveFalse_checkDataManagerCallsRemainTh
             IDataManager.StatEntry entry = dataManager.statEntries.get(i);
             assertEquals(i + 1, entry.getStepCounter());
             assertEquals(i, entry.getEpochCounter());
-            assertEquals(1.0, entry.getReward(), 0.0);
+            assertEquals(79.0, entry.getReward(), 0.0);
         }
 
         assertEquals(10, dataManager.isSaveDataCallCount);
@@ -128,7 +127,7 @@ public void refac_withHistoryProcessor_isSaveTrue_checkDataManagerCallsRemainThe
             IDataManager.StatEntry entry = dataManager.statEntries.get(i);
             assertEquals(i + 1, entry.getStepCounter());
             assertEquals(i, entry.getEpochCounter());
-            assertEquals(1.0, entry.getReward(), 0.0);
+            assertEquals(79.0, entry.getReward(), 0.0);
         }
 
         assertEquals(1, dataManager.isSaveDataCallCount);
@@ -308,91 +307,6 @@ public void save(String filename) throws IOException {
         }
     }
 
-    public static class MockEncodable implements Encodable {
-
-        private final int value;
-
-        public MockEncodable(int value) {
-
-            this.value = value;
-        }
-
-        @Override
-        public double[] toArray() {
-            return new double[] { value };
-        }
-    }
-
-    public static class MockObservationSpace implements ObservationSpace {
-
-        @Override
-        public String getName() {
-            return null;
-        }
-
-        @Override
-        public int[] getShape() {
-            return new int[] { 1 };
-        }
-
-        @Override
-        public INDArray getLow() {
-            return null;
-        }
-
-        @Override
-        public INDArray getHigh() {
-            return null;
-        }
-    }
-
-    public static class MockMDP implements MDP<MockEncodable, Integer, DiscreteSpace> {
-
-        private final DiscreteSpace actionSpace;
-        private int currentObsValue = 0;
-        private final ObservationSpace observationSpace;
-
-        public MockMDP(ObservationSpace observationSpace) {
-            actionSpace = new DiscreteSpace(5);
-            this.observationSpace = observationSpace;
-        }
-
-        @Override
-        public ObservationSpace getObservationSpace() {
-            return observationSpace;
-        }
-
-        @Override
-        public DiscreteSpace getActionSpace() {
-            return actionSpace;
-        }
-
-        @Override
-        public MockEncodable reset() {
-            return new MockEncodable(++currentObsValue);
-        }
-
-        @Override
-        public void close() {
-
-        }
-
-        @Override
-        public StepReply<MockEncodable> step(Integer obs) {
-            return new StepReply<MockEncodable>(new MockEncodable(obs), (double)obs, isDone(), null);
-        }
-
-        @Override
-        public boolean isDone() {
-            return false;
-        }
-
-        @Override
-        public MDP newInstance() {
-            return null;
-        }
-    }
-
     public static class MockAsyncConfiguration implements AsyncConfiguration {
 
         private final int nStep;

rl4j/rl4j-core/src/test/java/org/deeplearning4j/rl4j/learning/sync/qlearning/discrete/QLearningDiscreteTest.java

@@ -0,0 +1,142 @@
+package org.deeplearning4j.rl4j.learning.sync.qlearning.discrete;
+
+import org.deeplearning4j.rl4j.learning.IHistoryProcessor;
+import org.deeplearning4j.rl4j.learning.sync.Transition;
+import org.deeplearning4j.rl4j.learning.sync.qlearning.QLearning;
+import org.deeplearning4j.rl4j.mdp.MDP;
+import org.deeplearning4j.rl4j.network.dqn.IDQN;
+import org.deeplearning4j.rl4j.space.DiscreteSpace;
+import org.deeplearning4j.rl4j.support.*;
+import org.deeplearning4j.rl4j.util.IDataManager;
+import org.junit.Test;
+import org.nd4j.linalg.api.ndarray.INDArray;
+import org.nd4j.linalg.factory.Nd4j;
+import org.nd4j.linalg.primitives.Pair;
+
+import java.util.ArrayList;
+import java.util.List;
+
+import static org.junit.Assert.*;
+
+public class QLearningDiscreteTest {
+    @Test
+    public void refac_QLearningDiscrete_trainStep() {
+        // Arrange
+        MockObservationSpace observationSpace = new MockObservationSpace();
+        MockMDP mdp = new MockMDP(observationSpace);
+        MockDQN dqn = new MockDQN();
+        QLearning.QLConfiguration conf = new QLearning.QLConfiguration(0, 0, 0, 5, 1, 0,
+                0, 1.0, 0, 0, 0, 0, true);
+        MockDataManager dataManager = new MockDataManager(false);
+        TestQLearningDiscrete sut = new TestQLearningDiscrete(mdp, dqn, conf, dataManager, 10);
+        IHistoryProcessor.Configuration hpConf = new IHistoryProcessor.Configuration(5, 4, 4, 4, 4, 0, 0, 2);
+        MockHistoryProcessor hp = new MockHistoryProcessor(hpConf);
+        sut.setHistoryProcessor(hp);
+        MockExpReplay expReplay = new MockExpReplay();
+        sut.setExpReplay(expReplay);
+        MockEncodable obs = new MockEncodable(1);
+        List<QLearning.QLStepReturn<MockEncodable>> results = new ArrayList<>();
+
+        // Act
+        sut.initMdp();
+        for(int step = 0; step < 16; ++step) {
+            results.add(sut.trainStep(obs));
+            sut.incrementStep();
+        }
+
+        // Assert
+        // HistoryProcessor calls
+        assertEquals(24, hp.recordCallCount);
+        assertEquals(13, hp.addCallCount);
+        assertEquals(0, hp.startMonitorCallCount);
+        assertEquals(0, hp.stopMonitorCallCount);
+
+        // DQN calls
+        assertEquals(1, dqn.fitParams.size());
+        assertEquals(123.0, dqn.fitParams.get(0).getFirst().getDouble(0), 0.001);
+        assertEquals(234.0, dqn.fitParams.get(0).getSecond().getDouble(0), 0.001);
+        assertEquals(14, dqn.outputParams.size());
+        double[][] expectedDQNOutput = new double[][] {
+                new double[] { 0.0, 0.0, 0.0, 0.0, 1.0 },
+                new double[] { 0.0, 0.0, 0.0, 1.0, 9.0 },
+                new double[] { 0.0, 0.0, 0.0, 1.0, 9.0 },
+                new double[] { 0.0, 0.0, 1.0, 9.0, 11.0 },
+                new double[] { 0.0, 1.0, 9.0, 11.0, 13.0 },
+                new double[] { 0.0, 1.0, 9.0, 11.0, 13.0 },
+                new double[] { 1.0, 9.0, 11.0, 13.0, 15.0 },
+                new double[] { 1.0, 9.0, 11.0, 13.0, 15.0 },
+                new double[] { 9.0, 11.0, 13.0, 15.0, 17.0 },
+                new double[] { 9.0, 11.0, 13.0, 15.0, 17.0 },
+                new double[] { 11.0, 13.0, 15.0, 17.0, 19.0 },
+                new double[] { 11.0, 13.0, 15.0, 17.0, 19.0 },
+                new double[] { 13.0, 15.0, 17.0, 19.0, 21.0 },
+                new double[] { 13.0, 15.0, 17.0, 19.0, 21.0 },
+
+        };
+        for(int i = 0; i < expectedDQNOutput.length; ++i) {
+            INDArray outputParam = dqn.outputParams.get(i);
+
+            assertEquals(5, outputParam.shape()[0]);
+            assertEquals(1, outputParam.shape()[1]);
+
+            double[] expectedRow = expectedDQNOutput[i];
+            for(int j = 0; j < expectedRow.length; ++j) {
+                assertEquals(expectedRow[j] / 255.0, outputParam.getDouble(j), 0.00001);
+            }
+        }
+
+        // MDP calls
+        assertArrayEquals(new Integer[] { 0, 0, 0, 0, 0, 0, 0, 0, 0 ,0, 4, 4, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4 }, mdp.actions.toArray());
+
+        // ExpReplay calls
+        double[] expectedTrRewards = new double[] { 9.0, 21.0, 25.0, 29.0, 33.0, 37.0, 41.0, 45.0 };
+        int[] expectedTrActions = new int[] { 0, 4, 3, 4, 4, 4, 4, 4 };
+        double[] expectedTrNextObservation = new double[] { 0, 0, 0, 1.0, 9.0, 11.0, 13.0, 15.0 };
+        double[][] expectedTrObservations = new double[][] {
+                new double[] { 0.0, 0.0, 0.0, 0.0, 1.0 },
+                new double[] { 0.0, 0.0, 0.0, 1.0, 9.0 },
+                new double[] { 0.0, 0.0, 1.0, 9.0, 11.0 },
+                new double[] { 0.0, 1.0, 9.0, 11.0, 13.0 },
+                new double[] { 1.0, 9.0, 11.0, 13.0, 15.0 },
+                new double[] { 9.0, 11.0, 13.0, 15.0, 17.0 },
+                new double[] { 11.0, 13.0, 15.0, 17.0, 19.0 },
+                new double[] { 13.0, 15.0, 17.0, 19.0, 21.0 },
+        };
+        for(int i = 0; i < expectedTrRewards.length; ++i) {
+            Transition tr = expReplay.transitions.get(i);
+            assertEquals(expectedTrRewards[i], tr.getReward(), 0.0001);
+            assertEquals(expectedTrActions[i], tr.getAction());
+            assertEquals(expectedTrNextObservation[i], tr.getNextObservation().getDouble(0), 0.0001);
+            for(int j = 0; j < expectedTrObservations[i].length; ++j) {
+                assertEquals(expectedTrObservations[i][j], tr.getObservation()[j].getDouble(0), 0.0001);
+            }
+        }
+
+        // trainStep results
+        assertEquals(16, results.size());
+        double[] expectedMaxQ = new double[] { 1.0, 9.0, 11.0, 13.0, 15.0, 17.0, 19.0, 21.0 };
+        double[] expectedRewards = new double[] { 9.0, 11.0, 13.0, 15.0, 17.0, 19.0, 21.0, 23.0 };
+        for(int i=0; i < 16; ++i) {
+            QLearning.QLStepReturn<MockEncodable> result = results.get(i);
+            if(i % 2 == 0) {
+                assertEquals(expectedMaxQ[i/2] / 255.0, result.getMaxQ(), 0.001);
+                assertEquals(expectedRewards[i/2], result.getStepReply().getReward(), 0.001);
+            }
+            else {
+                assertTrue(result.getMaxQ().isNaN());
+            }
+        }
+    }
+
+    public static class TestQLearningDiscrete extends QLearningDiscrete<MockEncodable> {
+        public TestQLearningDiscrete(MDP<MockEncodable, Integer, DiscreteSpace> mdp,IDQN dqn,
+                                     QLConfiguration conf, IDataManager dataManager, int epsilonNbStep) {
+            super(mdp, dqn, conf, dataManager, epsilonNbStep);
+        }
+
+        @Override
+        protected Pair<INDArray, INDArray> setTarget(ArrayList<Transition<Integer>> transitions) {
+            return new Pair<>(Nd4j.create(new double[] { 123.0 }), Nd4j.create(new double[] { 234.0 }));
+        }
+    }
+}

rl4j/rl4j-core/src/test/java/org/deeplearning4j/rl4j/support/MockDQN.java

@@ -0,0 +1,100 @@
+package org.deeplearning4j.rl4j.support;
+
+import org.deeplearning4j.nn.api.NeuralNetwork;
+import org.deeplearning4j.nn.gradient.Gradient;
+import org.deeplearning4j.rl4j.network.NeuralNet;
+import org.deeplearning4j.rl4j.network.dqn.IDQN;
+import org.nd4j.linalg.api.ndarray.INDArray;
+import org.nd4j.linalg.primitives.Pair;
+
+import java.io.IOException;
+import java.io.OutputStream;
+import java.util.ArrayList;
+import java.util.List;
+
+public class MockDQN implements IDQN {
+
+    public final List<INDArray> outputParams = new ArrayList<>();
+    public final List<Pair<INDArray, INDArray>> fitParams = new ArrayList<>();
+
+    @Override
+    public NeuralNetwork[] getNeuralNetworks() {
+        return new NeuralNetwork[0];
+    }
+
+    @Override
+    public boolean isRecurrent() {
+        return false;
+    }
+
+    @Override
+    public void reset() {
+
+    }
+
+    @Override
+    public void fit(INDArray input, INDArray labels) {
+        fitParams.add(new Pair<>(input, labels));
+    }
+
+    @Override
+    public void fit(INDArray input, INDArray[] labels) {
+
+    }
+
+    @Override
+    public INDArray output(INDArray batch){
+        outputParams.add(batch);
+        return batch;
+    }
+
+    @Override
+    public INDArray[] outputAll(INDArray batch) {
+        return new INDArray[0];
+    }
+
+    @Override
+    public IDQN clone() {
+        return null;
+    }
+
+    @Override
+    public void copy(NeuralNet from) {
+
+    }
+
+    @Override
+    public void copy(IDQN from) {
+
+    }
+
+    @Override
+    public Gradient[] gradient(INDArray input, INDArray label) {
+        return new Gradient[0];
+    }
+
+    @Override
+    public Gradient[] gradient(INDArray input, INDArray[] label) {
+        return new Gradient[0];
+    }
+
+    @Override
+    public void applyGradient(Gradient[] gradient, int batchSize) {
+
+    }
+
+    @Override
+    public double getLatestScore() {
+        return 0;
+    }
+
+    @Override
+    public void save(OutputStream os) throws IOException {
+
+    }
+
+    @Override
+    public void save(String filename) throws IOException {
+
+    }
+}