Add td3 parameters policy_noise, noise_clip, policy_delay

src/main/grammars/de/monticore/lang/monticar/CNNTrain.mc4

@@ -215,8 +215,13 @@ grammar CNNTrain extends de.monticore.lang.monticar.Common2, de.monticore.Number
     UseDoubleDQNEntry implements ConfigEntry = name:"use_double_dqn" ":" value:BooleanValue;
 
 
-    // DDPG exclusive parameters
+    // DDPG and TD3 exclusive parameters
     CriticNetworkEntry implements ConfigEntry = name:"critic" ":" value:ComponentNameValue;
     SoftTargetUpdateRateEntry implements ConfigEntry = name:"soft_target_update_rate" ":" value:NumberValue;
     CriticOptimizerEntry implements ConfigEntry = name:"critic_optimizer" ":" value:OptimizerValue;
+
+    // TD3 exclusive parameters
+    PolicyNoiseEntry implements ConfigEntry = name:"policy_noise" ":" value:NumberValue;
+    NoiseClipEntry implements ConfigEntry = name:"noise_clip" ":" value:NumberValue;
+    PolicyDelayEntry implements ConfigEntry = name:"policy_delay" ":" value:IntegerValue;
 }
\ No newline at end of file

src/main/java/de/monticore/lang/monticar/cnntrain/_cocos/ParameterAlgorithmMapping.java

@@ -108,7 +108,17 @@ class ParameterAlgorithmMapping {
         ASTStrategyOUSigma.class
     );
 
-    private static final List<Class> EXCLUSIVE_TD3_PARAMETERS = Lists.newArrayList(EXCLUSIVE_DDPG_PARAMETERS);
+    private static final List<Class> EXCLUSIVE_TD3_PARAMETERS = Lists.newArrayList(
+        ASTCriticNetworkEntry.class,
+        ASTSoftTargetUpdateRateEntry.class,
+        ASTCriticOptimizerEntry.class,
+        ASTStrategyOUMu.class,
+        ASTStrategyOUTheta.class,
+        ASTStrategyOUSigma.class,
+        ASTPolicyNoiseEntry.class,
+        ASTNoiseClipEntry.class,
+        ASTPolicyDelayEntry.class
+    );
 
     ParameterAlgorithmMapping() {
 
@@ -118,7 +128,8 @@ class ParameterAlgorithmMapping {
         return GENERAL_PARAMETERS.contains(entryClazz)
             || GENERAL_REINFORCEMENT_PARAMETERS.contains(entryClazz)
             || EXCLUSIVE_DQN_PARAMETERS.contains(entryClazz)
-            || EXCLUSIVE_DDPG_PARAMETERS.contains(entryClazz);
+            || EXCLUSIVE_DDPG_PARAMETERS.contains(entryClazz)
+            || EXCLUSIVE_TD3_PARAMETERS.contains(entryClazz);
     }
 
     boolean isSupervisedLearningParameter(Class<? extends ASTEntry> entryClazz) {

src/main/java/de/monticore/lang/monticar/cnntrain/_symboltable/CNNTrainSymbolTableCreator.java

@@ -516,6 +516,30 @@ public class CNNTrainSymbolTableCreator extends CNNTrainSymbolTableCreatorTOP {
         configuration.getEntryMap().put(node.getName(), entry);
     }
 
+    @Override
+    public void visit(ASTPolicyNoiseEntry node) {
+        EntrySymbol entry = new EntrySymbol(node.getName());
+        entry.setValue(getValueSymbolForDouble(node.getValue()));
+        addToScopeAndLinkWithNode(entry, node);
+        configuration.getEntryMap().put(node.getName(), entry);
+    }
+
+    @Override
+    public void visit(ASTNoiseClipEntry node) {
+        EntrySymbol entry = new EntrySymbol(node.getName());
+        entry.setValue(getValueSymbolForDouble(node.getValue()));
+        addToScopeAndLinkWithNode(entry, node);
+        configuration.getEntryMap().put(node.getName(), entry);
+    }
+
+    @Override
+    public void visit(ASTPolicyDelayEntry node) {
+        EntrySymbol entry = new EntrySymbol(node.getName());
+        entry.setValue(getValueSymbolForInteger(node.getValue()));
+        addToScopeAndLinkWithNode(entry, node);
+        configuration.getEntryMap().put(node.getName(), entry);
+    }
+
     private void processMultiParamConfigVisit(ASTMultiParamConfigEntry node, Object value) {
         EntrySymbol entry = new EntrySymbol(node.getName());
         MultiParamValueSymbol valueSymbol = new MultiParamValueSymbol();

src/test/resources/valid_tests/TD3Config.cnnt

@@ -4,6 +4,10 @@ configuration TD3Config {
     critic : path.to.component
     environment : gym { name:"CartPole-v1" }
     soft_target_update_rate: 0.001
+    policy_noise: 0.2
+    noise_clip: 0.5
+    policy_delay: 2
+
     actor_optimizer : adam{
         learning_rate : 0.0001
         learning_rate_minimum : 0.00005