add test.

Signed-off-by: Paul Dubs <[email protected]>

Author: RobAltena

rl4j-ale-examples/pom.xml

@@ -63,9 +63,9 @@
             <version>${rl4j.version}</version>
         </dependency>
         <dependency>
-            <groupId>junit</groupId>
-            <artifactId>junit</artifactId>
-            <version>3.8.1</version>
+            <groupId>org.junit.jupiter</groupId>
+            <artifactId>junit-jupiter-engine</artifactId>
+            <version>5.4.2</version>
             <scope>test</scope>
         </dependency>
         <dependency>

rl4j-ale-examples/src/main/java/PlayALE.java

@@ -6,7 +6,7 @@
 
 public class PlayALE {
     public static void main(String[] args) throws IOException {
-        ALEMDP mdp = new ALEMDP("pong.bin");
+        ALEMDP mdp = new ALEMDP("E:\\projects\\ArcadeLearningEnvironment\\pong.bin");
 
         //load the previous agent
         ACPolicy<ALEMDP.GameScreen> pol2 = ACPolicy.load("ale-a3c.model");

rl4j-ale-examples/src/test/java/AleTest.java

@@ -0,0 +1,73 @@
+import org.deeplearning4j.nn.api.NeuralNetwork;
+import org.deeplearning4j.nn.graph.ComputationGraph;
+import org.deeplearning4j.rl4j.learning.HistoryProcessor;
+import org.deeplearning4j.rl4j.learning.async.a3c.discrete.A3CDiscrete;
+import org.deeplearning4j.rl4j.learning.async.a3c.discrete.A3CDiscreteConv;
+import org.deeplearning4j.rl4j.mdp.ale.ALEMDP;
+import org.deeplearning4j.rl4j.network.ac.ActorCriticFactoryCompGraphStdConv;
+import org.deeplearning4j.rl4j.policy.ACPolicy;
+import org.junit.jupiter.api.Test;
+import org.nd4j.linalg.api.ndarray.INDArray;
+import org.nd4j.linalg.factory.Nd4j;
+import org.nd4j.linalg.learning.config.Adam;
+
+import java.io.IOException;
+
+public class AleTest {
+
+    @Test
+    public void TrainModelDataShape(){
+
+        // Set up the training as in the sample.
+        HistoryProcessor.Configuration ALE_HP = new HistoryProcessor.Configuration(
+                4,       //History length
+                84,      //resize width
+                110,     //resize height
+                84,      //crop width
+                84,      //crop height
+                0,       //cropping x offset
+                0,       //cropping y offset
+                4        //skip mod (one frame is picked every x
+        );
+
+        A3CDiscrete.A3CConfiguration ALE_A3C = new A3CDiscrete.A3CConfiguration(
+                123,            //Random seed
+                10000,          //Max step By epoch
+                8000000,        //Max step
+                8,              //Number of threads
+                32,             //t_max
+                500,            //num step noop warmup
+                0.1,            //reward scaling
+                0.99,           //gamma
+                10.0            //td-error clipping
+        );
+
+        final ActorCriticFactoryCompGraphStdConv.Configuration ALE_NET_A3C =
+                new ActorCriticFactoryCompGraphStdConv.Configuration(
+                        0.000,   //l2 regularization
+                        new Adam(0.00025), //learning rate
+                        null, false
+                );
+        ALEMDP mdp = new ALEMDP("pong.bin");
+        A3CDiscreteConv<ALEMDP.GameScreen> a3c = new A3CDiscreteConv<ALEMDP.GameScreen>(mdp, ALE_NET_A3C, ALE_HP, ALE_A3C);
+
+        NeuralNetwork [] nns  = a3c.getNeuralNet().getNeuralNetworks();
+        ComputationGraph g = (ComputationGraph ) nns[0];
+
+        // Now pass in some dummy data in the expected shape.
+        INDArray dummy = Nd4j.rand( 1,4, 84, 84);
+        g.output(new INDArray[] {dummy}); //If we get the shape wrong we crash here.
+    }
+
+    @Test
+    void LoadModel() throws IOException {
+        //load the previous agent
+        ACPolicy<ALEMDP.GameScreen> pol = ACPolicy.load("ale-a3c.model");
+        NeuralNetwork [] nns  = pol.getNeuralNet().getNeuralNetworks();
+        ComputationGraph g = (ComputationGraph ) nns[0];
+
+        // Now pass in some dummy data in the expected shape.
+        INDArray dummy = Nd4j.rand( 1,4, 84, 84);
+        g.output(new INDArray[] {dummy}); //If we get the shape wrong we crash here.
+    }
+}