Add parameter for gaussian strategy

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

@@ -176,11 +176,14 @@ grammar CNNTrain extends de.monticore.lang.monticar.Common2, de.monticore.Number
     interface StrategyOrnsteinUhlenbeckEntry extends Entry;
     StrategyOrnsteinUhlenbeckValue implements StrategyValue = name:"ornstein_uhlenbeck" ("{" params:StrategyOrnsteinUhlenbeckEntry* "}")?;
 
+    interface StrategyGaussianEntry extends Entry;
+    StrategyGaussianValue implements StrategyValue = name:"gaussian" ("{" params:StrategyGaussianEntry* "}")?;
+
     StrategyOUMu implements StrategyOrnsteinUhlenbeckEntry = name: "mu" ":" value:DoubleVectorValue;
     StrategyOUTheta implements StrategyOrnsteinUhlenbeckEntry = name: "theta" ":" value:DoubleVectorValue;
     StrategyOUSigma implements StrategyOrnsteinUhlenbeckEntry = name: "sigma" ":" value:DoubleVectorValue;
 
-    interface GeneralStrategyEntry extends StrategyEpsGreedyEntry, StrategyOrnsteinUhlenbeckEntry;
+    interface GeneralStrategyEntry extends StrategyEpsGreedyEntry, StrategyOrnsteinUhlenbeckEntry, StrategyGaussianEntry;
 
     GreedyEpsilonEntry implements GeneralStrategyEntry = name:"epsilon" ":" value:NumberValue;
     MinEpsilonEntry implements GeneralStrategyEntry = name:"min_epsilon" ":" value:NumberValue;

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

@@ -30,6 +30,7 @@ import java.util.Set;
 public class CheckContinuousRLAlgorithmUsesContinuousStrategy implements CNNTrainASTConfigurationCoCo{
     private static final Set<String> CONTINUOUS_STRATEGIES = ImmutableSet.<String>builder()
         .add("ornstein_uhlenbeck")
+        .add("gaussian")
         .build();
 
     @Override

src/test/java/de/monticore/lang/monticar/cnntrain/cocos/AllCoCoTest.java

@@ -42,6 +42,7 @@ public class AllCoCoTest extends AbstractCoCoTest{
         checkValid("valid_tests", "ReinforcementConfig");
         checkValid("valid_tests", "ReinforcementConfig2");
         checkValid("valid_tests", "DdpgConfig");
+        checkValid("valid_tests", "TD3Config");
         checkValid("valid_tests", "ReinforcementWithRosReward");
     }
 

src/test/resources/valid_tests/TD3Config.cnnt

+configuration TD3Config {
+    learning_method : reinforcement
+    rl_algorithm : ddpg-algorithm
+    critic : path.to.component
+    environment : gym { name:"CartPole-v1" }
+    soft_target_update_rate: 0.001
+    actor_optimizer : adam{
+        learning_rate : 0.0001
+        learning_rate_minimum : 0.00005
+        learning_rate_decay : 0.9
+        learning_rate_policy : step
+    }
+    critic_optimizer : rmsprop{
+        learning_rate : 0.001
+        learning_rate_minimum : 0.0001
+        learning_rate_decay : 0.5
+        learning_rate_policy : step
+    }
+    strategy : gaussian {
+        epsilon: 1.0
+        min_epsilon: 0.001
+        epsilon_decay_method: linear
+        epsilon_decay : 0.0001
+        epsilon_decay_start: 50
+    }
+}
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