changed version

added loss functions

pom.xml

@@ -8,7 +8,7 @@
 
   <groupId>de.monticore.lang.monticar</groupId>
   <artifactId>cnnarch-gluon-generator</artifactId>
-  <version>0.2.0-SNAPSHOT</version>
+  <version>0.2.2-SNAPSHOT</version>
 
   <!-- == PROJECT DEPENDENCIES ============================================= -->
 
@@ -16,8 +16,8 @@
 
     <!-- .. SE-Libraries .................................................. -->
       <CNNArch.version>0.3.0-SNAPSHOT</CNNArch.version>
-      <CNNTrain.version>0.3.1-SNAPSHOT</CNNTrain.version>
-      <CNNArch2MXNet.version>0.2.14-SNAPSHOT</CNNArch2MXNet.version>
+      <CNNTrain.version>0.3.4-SNAPSHOT</CNNTrain.version>
+      <CNNArch2MXNet.version>0.2.16-SNAPSHOT</CNNArch2MXNet.version>
       <embedded-montiarc-math-opt-generator>0.1.4</embedded-montiarc-math-opt-generator>
       <EMADL2PythonWrapper.version>0.0.1</EMADL2PythonWrapper.version>
     

src/main/java/de/monticore/lang/monticar/cnnarch/gluongenerator/CNNArch2Gluon.java

@@ -20,11 +20,13 @@
  */
 package de.monticore.lang.monticar.cnnarch.gluongenerator;
 
+import de.monticore.lang.monticar.cnnarch._symboltable.IOSymbol;
 import de.monticore.lang.monticar.cnnarch.mxnetgenerator.CNNArch2MxNet;
 import de.monticore.lang.monticar.cnnarch.mxnetgenerator.Target;
 
 import de.monticore.lang.monticar.cnnarch._symboltable.ArchitectureSymbol;
 import de.monticore.lang.monticar.cnnarch.mxnetgenerator.TemplateConfiguration;
+import de.se_rwth.commons.logging.Log;
 
 import java.util.HashMap;
 import java.util.Map;
@@ -34,35 +36,81 @@ public class CNNArch2Gluon extends CNNArch2MxNet {
     //check cocos with CNNArchCocos.checkAll(architecture) before calling this method.
     @Override
     public Map<String, String> generateStrings(ArchitectureSymbol architecture){
-        TemplateConfiguration templateConfiguration = new GluonTemplateConfiguration();
+        Map<String, String> fileContentMap = compileFileContentMap(architecture);
+        checkValidGeneration(architecture);
+        return fileContentMap;
+    }
+
+    public Map<String, String> generateStringsAllowMultipleIO(ArchitectureSymbol architecture, Boolean pythonFilesOnly) {
+        Map<String, String> fileContentMap;
+        if (pythonFilesOnly) {
+            fileContentMap = compilePythonFilesOnlyContentMap(architecture);
+        } else {
+            fileContentMap = compileFileContentMap(architecture);
+        }
+        checkValidOutputTypes(architecture);
+        return fileContentMap;
+    }
 
+    private void checkValidOutputTypes(ArchitectureSymbol architecture) {
+        if (((IOSymbol)architecture.getOutputs().get(0)).getDefinition().getType().getWidth() != 1
+            || ((IOSymbol)architecture.getOutputs().get(0)).getDefinition().getType().getHeight() != 1) {
+            Log.error("This cnn architecture has a multi-dimensional output, which is currently not supported by" +
+                " the code generator.", architecture.getSourcePosition());
+        }
+    }
+
+    private Map<String, String> compilePythonFiles(CNNArch2GluonTemplateController controller, ArchitectureSymbol architecture) {
         Map<String, String> fileContentMap = new HashMap<>();
-        CNNArch2GluonTemplateController archTc = new CNNArch2GluonTemplateController(
-                architecture, templateConfiguration);
         Map.Entry<String, String> temp;
 
-        temp = archTc.process("CNNPredictor", Target.CPP);
-        fileContentMap.put(temp.getKey(), temp.getValue());
-
-        temp = archTc.process("CNNNet", Target.PYTHON);
+        temp = controller.process("CNNNet", Target.PYTHON);
         fileContentMap.put(temp.getKey(), temp.getValue());
 
         if (architecture.getDataPath() != null) {
-            temp = archTc.process("CNNDataLoader", Target.PYTHON);
+            temp = controller.process("CNNDataLoader", Target.PYTHON);
             fileContentMap.put(temp.getKey(), temp.getValue());
         }
 
-        temp = archTc.process("CNNCreator", Target.PYTHON);
+        temp = controller.process("CNNCreator", Target.PYTHON);
+        fileContentMap.put(temp.getKey(), temp.getValue());
+
+        return fileContentMap;
+    }
+
+    private Map<String, String> compileCppFiles(CNNArch2GluonTemplateController controller) {
+        Map<String, String> fileContentMap = new HashMap<>();
+        Map.Entry<String, String> temp;
+
+        temp = controller.process("CNNPredictor", Target.CPP);
         fileContentMap.put(temp.getKey(), temp.getValue());
 
-        temp = archTc.process("execute", Target.CPP);
+        temp = controller.process("execute", Target.CPP);
         fileContentMap.put(temp.getKey().replace(".h", ""), temp.getValue());
 
-        temp = archTc.process("CNNBufferFile", Target.CPP);
+        temp = controller.process("CNNBufferFile", Target.CPP);
         fileContentMap.put("CNNBufferFile.h", temp.getValue());
 
-        checkValidGeneration(architecture);
+        return fileContentMap;
+    }
+
+    private Map<String, String> compileFileContentMap(ArchitectureSymbol architecture) {
+        TemplateConfiguration templateConfiguration = new GluonTemplateConfiguration();
+
+        Map<String, String> fileContentMap = new HashMap<>();
+        CNNArch2GluonTemplateController archTc = new CNNArch2GluonTemplateController(
+                architecture, templateConfiguration);
+
+        fileContentMap.putAll(compilePythonFiles(archTc, architecture));
+        fileContentMap.putAll(compileCppFiles(archTc));
 
         return fileContentMap;
     }
-}
+
+    private Map<String, String> compilePythonFilesOnlyContentMap(ArchitectureSymbol architecture) {
+        TemplateConfiguration templateConfiguration = new GluonTemplateConfiguration();
+        CNNArch2GluonTemplateController archTc = new CNNArch2GluonTemplateController(
+                architecture, templateConfiguration);
+        return compilePythonFiles(archTc, architecture);
+    }
+}
\ No newline at end of file

src/main/java/de/monticore/lang/monticar/cnnarch/gluongenerator/CNNTrain2Gluon.java

 package de.monticore.lang.monticar.cnnarch.gluongenerator;
 
 import com.google.common.collect.Maps;
+import de.monticore.lang.monticar.cnnarch.gluongenerator.reinforcement.critic.CriticNetworkGenerationPair;
+import de.monticore.lang.monticar.cnnarch.gluongenerator.reinforcement.critic.CriticNetworkGenerator;
 import de.monticore.lang.monticar.cnnarch.gluongenerator.reinforcement.FunctionParameterChecker;
 import de.monticore.lang.monticar.cnnarch.gluongenerator.reinforcement.RewardFunctionParameterAdapter;
 import de.monticore.lang.monticar.cnnarch.gluongenerator.reinforcement.RewardFunctionSourceGenerator;
@@ -10,7 +12,9 @@ import de.monticore.lang.monticar.cnnarch.mxnetgenerator.CNNTrain2MxNet;
 import de.monticore.lang.monticar.cnnarch.mxnetgenerator.TemplateConfiguration;
 import de.monticore.lang.monticar.cnntrain._symboltable.ConfigurationSymbol;
 import de.monticore.lang.monticar.cnntrain._symboltable.LearningMethod;
+import de.monticore.lang.monticar.cnntrain._symboltable.RLAlgorithm;
 import de.monticore.lang.monticar.cnntrain._symboltable.RewardFunctionSymbol;
+import de.monticore.lang.monticar.cnntrain.annotations.TrainedArchitecture;
 import de.monticore.lang.monticar.generator.FileContent;
 import de.monticore.lang.monticar.generator.cpp.GeneratorCPP;
 import de.monticore.lang.monticar.generator.pythonwrapper.GeneratorPythonWrapperStandaloneApi;
@@ -25,6 +29,7 @@ import java.nio.file.Files;
 import java.nio.file.Path;
 import java.nio.file.Paths;
 import java.util.*;
+import java.util.stream.Collectors;
 
 public class CNNTrain2Gluon extends CNNTrain2MxNet {
     private static final String REINFORCEMENT_LEARNING_FRAMEWORK_MODULE = "reinforcement_learning";
@@ -62,6 +67,15 @@ public class CNNTrain2Gluon extends CNNTrain2MxNet {
     public void generate(Path modelsDirPath, String rootModelName) {
         ConfigurationSymbol configuration = this.getConfigurationSymbol(modelsDirPath, rootModelName);
 
+        if (configuration.getLearningMethod().equals(LearningMethod.REINFORCEMENT)) {
+            throw new IllegalStateException("Cannot call generate of reinforcement configuration without specifying " +
+             "the trained architecture");
+        }
+
+        generateFilesFromConfigurationSymbol(configuration);
+    }
+
+    private void generateFilesFromConfigurationSymbol(ConfigurationSymbol configuration) {
         Map<String, String> fileContents = this.generateStrings(configuration);
         GeneratorCPP genCPP = new GeneratorCPP();
         genCPP.setGenerationTargetPath(this.getGenerationTargetPath());
@@ -78,6 +92,13 @@ public class CNNTrain2Gluon extends CNNTrain2MxNet {
         }
     }
 
+    public void generate(Path modelsDirPath, String rootModelName, TrainedArchitecture trainedArchitecture) {
+        ConfigurationSymbol configurationSymbol = this.getConfigurationSymbol(modelsDirPath, rootModelName);
+        configurationSymbol.setTrainedArchitecture(trainedArchitecture);
+        this.setRootProjectModelsDir(modelsDirPath.toString());
+        generateFilesFromConfigurationSymbol(configurationSymbol);
+    }
+
     @Override
     public Map<String, String> generateStrings(ConfigurationSymbol configuration) {
         TemplateConfiguration templateConfiguration = new GluonTemplateConfiguration();
@@ -98,8 +119,30 @@ public class CNNTrain2Gluon extends CNNTrain2MxNet {
             fileContentMap.put("supervised_trainer.py", cnnSupervisedTrainerContent);
         } else if (configData.isReinforcementLearning()) {
             final String trainerName = "CNNTrainer_" + getInstanceName();
+            final RLAlgorithm rlAlgorithm = configData.getRlAlgorithm();
+
+            if (rlAlgorithm.equals(RLAlgorithm.DDPG)) {
+                CriticNetworkGenerator criticNetworkGenerator = new CriticNetworkGenerator();
+                criticNetworkGenerator.setGenerationTargetPath(
+                    Paths.get(getGenerationTargetPath(), REINFORCEMENT_LEARNING_FRAMEWORK_MODULE).toString());
+                if (getRootProjectModelsDir().isPresent()) {
+                    criticNetworkGenerator.setRootModelsDir(getRootProjectModelsDir().get());
+                } else {
+                    Log.error("No root model dir set");
+                }
+
+                CriticNetworkGenerationPair criticNetworkResult
+                    = criticNetworkGenerator.generateCriticNetworkContent(templateConfiguration, configuration);
+
+                fileContentMap.putAll(criticNetworkResult.getFileContent().entrySet().stream().collect(Collectors.toMap(
+                            k -> REINFORCEMENT_LEARNING_FRAMEWORK_MODULE + "/" + k.getKey(),
+                            Map.Entry::getValue))
+                );
+                ftlContext.put("criticInstanceName", criticNetworkResult.getCriticNetworkName());
+            }
+
             ftlContext.put("trainerName", trainerName);
-            Map<String, String> rlFrameworkContentMap = constructReinforcementLearningFramework(templateConfiguration, ftlContext);
+            Map<String, String> rlFrameworkContentMap = constructReinforcementLearningFramework(templateConfiguration, ftlContext, rlAlgorithm);
             fileContentMap.putAll(rlFrameworkContentMap);
 
             final String reinforcementTrainerContent = templateConfiguration.processTemplate(ftlContext, "reinforcement/Trainer.ftl");
@@ -161,23 +204,34 @@ public class CNNTrain2Gluon extends CNNTrain2MxNet {
     }
 
     private Map<String, String> constructReinforcementLearningFramework(
-            final TemplateConfiguration templateConfiguration, final Map<String, Object> ftlContext) {
+            final TemplateConfiguration templateConfiguration,
+            final Map<String, Object> ftlContext,
+            RLAlgorithm rlAlgorithm) {
         Map<String, String> fileContentMap = Maps.newHashMap();
         ftlContext.put("rlFrameworkModule", REINFORCEMENT_LEARNING_FRAMEWORK_MODULE);
 
-        final String reinforcementAgentContent = templateConfiguration.processTemplate(ftlContext, "reinforcement/agent/Agent.ftl");
+        final String loggerContent = templateConfiguration.processTemplate(ftlContext,
+                "reinforcement/util/Logger.ftl");
+        fileContentMap.put(REINFORCEMENT_LEARNING_FRAMEWORK_MODULE + "/cnnarch_logger.py", loggerContent);
+
+        final String reinforcementAgentContent = templateConfiguration.processTemplate(ftlContext,
+                "reinforcement/agent/Agent.ftl");
         fileContentMap.put(REINFORCEMENT_LEARNING_FRAMEWORK_MODULE + "/agent.py", reinforcementAgentContent);
 
-        final String reinforcementPolicyContent = templateConfiguration.processTemplate(ftlContext, "reinforcement/agent/ActionPolicy.ftl");
-        fileContentMap.put(REINFORCEMENT_LEARNING_FRAMEWORK_MODULE + "/action_policy.py", reinforcementPolicyContent);
+        final String reinforcementStrategyContent = templateConfiguration.processTemplate(
+            ftlContext, "reinforcement/agent/Strategy.ftl");
+        fileContentMap.put(REINFORCEMENT_LEARNING_FRAMEWORK_MODULE + "/strategy.py", reinforcementStrategyContent);
 
-        final String replayMemoryContent = templateConfiguration.processTemplate(ftlContext, "reinforcement/agent/ReplayMemory.ftl");
+        final String replayMemoryContent = templateConfiguration.processTemplate(
+            ftlContext, "reinforcement/agent/ReplayMemory.ftl");
         fileContentMap.put(REINFORCEMENT_LEARNING_FRAMEWORK_MODULE + "/replay_memory.py", replayMemoryContent);
 
-        final String environmentContent = templateConfiguration.processTemplate(ftlContext, "reinforcement/environment/Environment.ftl");
+        final String environmentContent = templateConfiguration.processTemplate(
+            ftlContext, "reinforcement/environment/Environment.ftl");
         fileContentMap.put(REINFORCEMENT_LEARNING_FRAMEWORK_MODULE + "/environment.py", environmentContent);
 
-        final String utilContent = templateConfiguration.processTemplate(ftlContext, "reinforcement/util/Util.ftl");
+        final String utilContent = templateConfiguration.processTemplate(
+            ftlContext, "reinforcement/util/Util.ftl");
         fileContentMap.put(REINFORCEMENT_LEARNING_FRAMEWORK_MODULE + "/util.py", utilContent);
 
         final String initContent = "";

src/main/java/de/monticore/lang/monticar/cnnarch/gluongenerator/ReinforcementConfigurationData.java

 package de.monticore.lang.monticar.cnnarch.gluongenerator;
 
+import de.monticore.lang.monticar.cnnarch._symboltable.ArchitectureSymbol;
 import de.monticore.lang.monticar.cnnarch.gluongenerator.reinforcement.RewardFunctionParameterAdapter;
 import de.monticore.lang.monticar.cnnarch.mxnetgenerator.ConfigurationData;
 import de.monticore.lang.monticar.cnntrain._symboltable.*;
+import de.monticore.lang.monticar.cnntrain.annotations.Range;
+import de.monticore.lang.monticar.cnntrain.annotations.TrainedArchitecture;
 
-import java.util.HashMap;
-import java.util.List;
-import java.util.Map;
-import java.util.Optional;
+import java.util.*;
 
 /**
  *
@@ -25,10 +25,19 @@ public class ReinforcementConfigurationData extends ConfigurationData {
     private static final String AST_ENTRY_AGENT_NAME = "agent_name";
     private static final String AST_ENTRY_USE_DOUBLE_DQN = "use_double_dqn";
     private static final String AST_ENTRY_LOSS = "loss";
-
+    private static final String AST_ENTRY_RL_ALGORITHM = "rl_algorithm";
     private static final String AST_ENTRY_REPLAY_MEMORY = "replay_memory";
-    private static final String AST_ENTRY_ACTION_SELECTION = "action_selection";
+    private static final String AST_ENTRY_STRATEGY = "strategy";
     private static final String AST_ENTRY_ENVIRONMENT = "environment";
+    private static final String AST_ENTRY_START_TRAINING_AT = "start_training_at";
+    private static final String AST_SOFT_TARGET_UPDATE_RATE = "soft_target_update_rate";
+    private static final String AST_EVALUATION_SAMPLES = "evaluation_samples";
+
+    private static final String ENVIRONMENT_PARAM_REWARD_TOPIC = "reward_topic";
+    private static final String ENVIRONMENT_ROS = "ros_interface";
+    private static final String ENVIRONMENT_GYM = "gym";
+
+    private static final String STRATEGY_ORNSTEIN_UHLENBECK = "ornstein_uhlenbeck";
 
     public ReinforcementConfigurationData(ConfigurationSymbol configuration, String instanceName) {
         super(configuration, instanceName);
@@ -97,6 +106,67 @@ public class ReinforcementConfigurationData extends ConfigurationData {
                 ? null : (Boolean)retrieveConfigurationEntryValueByKey(AST_ENTRY_USE_DOUBLE_DQN);
     }
 
+    public Double getSoftTargetUpdateRate() {
+        return !configurationContainsKey(AST_SOFT_TARGET_UPDATE_RATE)
+                ? null : (Double)retrieveConfigurationEntryValueByKey(AST_SOFT_TARGET_UPDATE_RATE);
+    }
+
+    public Integer getStartTrainingAt() {
+        return !configurationContainsKey(AST_ENTRY_START_TRAINING_AT)
+            ? null : (Integer)retrieveConfigurationEntryValueByKey(AST_ENTRY_START_TRAINING_AT);
+    }
+
+    public Integer getEvaluationSamples() {
+        return !configurationContainsKey(AST_EVALUATION_SAMPLES)
+            ? null : (Integer)retrieveConfigurationEntryValueByKey(AST_EVALUATION_SAMPLES);
+    }
+
+
+
+    public RLAlgorithm getRlAlgorithm() {
+        if (!isReinforcementLearning()) {
+            return null;
+        }
+        return !configurationContainsKey(AST_ENTRY_RL_ALGORITHM)
+            ? RLAlgorithm.DQN : (RLAlgorithm)retrieveConfigurationEntryValueByKey(AST_ENTRY_RL_ALGORITHM);
+    }
+
+    public String getInputNameOfTrainedArchitecture() {
+        if (!this.getConfiguration().getTrainedArchitecture().isPresent()) {
+            throw new IllegalStateException("No trained architecture set");
+        }
+        TrainedArchitecture trainedArchitecture = getConfiguration().getTrainedArchitecture().get();
+        // We allow only one input, the first one is the only input
+        return trainedArchitecture.getInputs().get(0);
+    }
+
+    public String getOutputNameOfTrainedArchitecture() {
+        if (!this.getConfiguration().getTrainedArchitecture().isPresent()) {
+            throw new IllegalStateException("No trained architecture set");
+        }
+        TrainedArchitecture trainedArchitecture = getConfiguration().getTrainedArchitecture().get();
+        // We allow only one output, the first one is the only output
+        return trainedArchitecture.getOutputs().get(0);
+    }
+
+    public List<Integer> getStateDim() {
+        if (!this.getConfiguration().getTrainedArchitecture().isPresent()) {
+            return null;
+        }
+        final String inputName = getInputNameOfTrainedArchitecture();
+        TrainedArchitecture trainedArchitecture = this.getConfiguration().getTrainedArchitecture().get();
+        return trainedArchitecture.getDimensions().get(inputName);
+    }
+
+    public List<Integer> getActionDim() {
+        if (!this.getConfiguration().getTrainedArchitecture().isPresent()) {
+            return null;
+        }
+        final String outputName = getOutputNameOfTrainedArchitecture();
+        TrainedArchitecture trainedArchitecture = this.getConfiguration().getTrainedArchitecture().get();
+        return trainedArchitecture.getDimensions().get(outputName);
+    }
+
     public String getLoss() {
         return !configurationContainsKey(AST_ENTRY_LOSS)
                 ? null : retrieveConfigurationEntryValueByKey(AST_ENTRY_LOSS).toString();
@@ -106,8 +176,36 @@ public class ReinforcementConfigurationData extends ConfigurationData {
         return getMultiParamEntry(AST_ENTRY_REPLAY_MEMORY, "method");
     }
 
-    public Map<String, Object> getActionSelection() {
-        return getMultiParamEntry(AST_ENTRY_ACTION_SELECTION, "method");
+    public Map<String, Object> getStrategy() {
+        assert isReinforcementLearning(): "Strategy parameter only for reinforcement learning but called in a " +
+         " non reinforcement learning context";
+        Map<String, Object> strategyParams = getMultiParamEntry(AST_ENTRY_STRATEGY, "method");
+        if (strategyParams.get("method").equals(STRATEGY_ORNSTEIN_UHLENBECK)) {
+            assert getConfiguration().getTrainedArchitecture().isPresent(): "Architecture not present," +
+             " but reinforcement training";
+            TrainedArchitecture trainedArchitecture = getConfiguration().getTrainedArchitecture().get();
+            final String actionPortName = getOutputNameOfTrainedArchitecture();
+            Range actionRange = trainedArchitecture.getRanges().get(actionPortName);
+
+            if (actionRange.isLowerLimitInfinity() && actionRange.isUpperLimitInfinity()) {
+                strategyParams.put("action_low", null);
+                strategyParams.put("action_high", null);
+            } else if(!actionRange.isLowerLimitInfinity() && actionRange.isUpperLimitInfinity()) {
+                assert actionRange.getLowerLimit().isPresent();
+                strategyParams.put("action_low", actionRange.getLowerLimit().get());
+                strategyParams.put("action_high", null);
+            } else if (actionRange.isLowerLimitInfinity() && !actionRange.isUpperLimitInfinity()) {
+                assert actionRange.getUpperLimit().isPresent();
+                strategyParams.put("action_low", null);
+                strategyParams.put("action_high", actionRange.getUpperLimit().get());
+            } else {
+                assert actionRange.getLowerLimit().isPresent();
+                assert actionRange.getUpperLimit().isPresent();
+                strategyParams.put("action_low", actionRange.getLowerLimit().get());
+                strategyParams.put("action_high", actionRange.getUpperLimit().get());
+            }
+        }
+        return strategyParams;
     }
 
     public Map<String, Object> getEnvironment() {
@@ -136,6 +234,16 @@ public class ReinforcementConfigurationData extends ConfigurationData {
                         .getRewardFunctionParameter().orElse(null));
     }
 
+    public boolean isDiscreteRlAlgorithm() {
+        assert isReinforcementLearning();
+        return getRlAlgorithm().equals(RLAlgorithm.DQN);
+    }
+
+    public boolean isContinuousRlAlgorithm() {
+        assert isReinforcementLearning();
+        return getRlAlgorithm().equals(RLAlgorithm.DDPG);
+    }
+
     public Map<String, Object> getRewardFunctionStateParameter() {
         if (!getRlRewardFunctionParameter().isPresent()
             || !getRlRewardFunctionParameter().get().getInputStateParameterName().isPresent()) {
@@ -159,6 +267,50 @@ public class ReinforcementConfigurationData extends ConfigurationData {
         return getRlRewardFunctionParameter().get().getOutputParameterName().orElse(null);
     }
 
+    public String getCriticOptimizerName() {
+        if (!getConfiguration().getCriticOptimizer().isPresent()) {
+            return null;
+        }
+        return getConfiguration().getCriticOptimizer().get().getName();
+    }
+
+    public Map<String, String> getCriticOptimizerParams() {
+        // get classes for single enum values
+        assert getConfiguration().getCriticOptimizer().isPresent():
+            "Critic optimizer params called although, not present";
+        List<Class> lrPolicyClasses = new ArrayList<>();
+        for (LRPolicy enum_value: LRPolicy.values()) {
+            lrPolicyClasses.add(enum_value.getClass());
+        }
+
+
+
+        Map<String, String>  mapToStrings = new HashMap<>();
+        Map<String, OptimizerParamSymbol> optimizerParams =
+            getConfiguration().getCriticOptimizer().get().getOptimizerParamMap();
+        for (Map.Entry<String, OptimizerParamSymbol> entry : optimizerParams.entrySet()) {
+            String paramName = entry.getKey();
+            String valueAsString = entry.getValue().toString();
+            Class realClass = entry.getValue().getValue().getValue().getClass();
+            if (realClass == Boolean.class) {
+                valueAsString = (Boolean) entry.getValue().getValue().getValue() ? "True" : "False";
+            } else if (lrPolicyClasses.contains(realClass)) {
+                valueAsString = "'" + valueAsString + "'";
+            }
+            mapToStrings.put(paramName, valueAsString);
+        }
+        return mapToStrings;
+    }
+
+    public boolean hasRosRewardTopic() {
+        Map<String, Object> environmentParameters = getMultiParamEntry(AST_ENTRY_ENVIRONMENT, "environment");
+        if (environmentParameters == null
+            || !environmentParameters.containsKey("environment")) {
+            return false;
+        }
+        return environmentParameters.containsKey(ENVIRONMENT_PARAM_REWARD_TOPIC);
+    }
+
     private Map<String, Object> getMultiParamEntry(final String key, final String valueName) {
         if (!configurationContainsKey(key)) {
             return null;

src/main/java/de/monticore/lang/monticar/cnnarch/gluongenerator/annotations/ArchitectureAdapter.java

+package de.monticore.lang.monticar.cnnarch.gluongenerator.annotations;
+
+import de.monticore.lang.monticar.cnnarch._symboltable.ArchitectureSymbol;
+import de.monticore.lang.monticar.cnnarch._symboltable.IOSymbol;
+import de.monticore.lang.monticar.cnntrain.annotations.Range;
+import de.monticore.lang.monticar.cnntrain.annotations.TrainedArchitecture;
+import de.monticore.lang.monticar.ranges._ast.ASTRange;
+import de.monticore.symboltable.CommonSymbol;
+
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Map;
+import java.util.stream.Collectors;
+
+import static com.google.common.base.Preconditions.checkNotNull;
+
+public class ArchitectureAdapter implements TrainedArchitecture {
+    private ArchitectureSymbol architectureSymbol;
+
+
+    public ArchitectureAdapter(final ArchitectureSymbol architectureSymbol) {
+        checkNotNull(architectureSymbol);
+        this.architectureSymbol = architectureSymbol;
+    }
+
+    @Override
+    public List<String> getInputs() {
+        return getIOInputSymbols().stream()
+            .map(CommonSymbol::getName)
+            .collect(Collectors.toList());
+    }
+
+    @Override
+    public List<String> getOutputs() {
+        return getIOOutputSymbols().stream()
+            .map(CommonSymbol::getName)
+            .collect(Collectors.toList());
+    }
+
+    @Override
+    public Map<String, List<Integer>> getDimensions() {
+        return getAllIOSymbols().stream().collect(Collectors.toMap(CommonSymbol::getName,
+            s-> s.getDefinition().getType().getDimensions()));
+    }
+
+    @Override
+    public Map<String, Range> getRanges() {
+        return getAllIOSymbols().stream().collect(Collectors.toMap(CommonSymbol::getName,
+            s -> astRangeToTrainRange(s.getDefinition().getType().getDomain().getRangeOpt().orElse(null))));
+    }
+
+    @Override