Adjust tests

src/test/java/de/monticore/lang/monticar/emadl/GenerationTest.java

@@ -214,11 +214,12 @@ public class GenerationTest extends AbstractSymtabTest {
                         "HelperA.h",
                         "start_training.sh",
                         "reinforcement_learning/__init__.py",
-                        "reinforcement_learning/action_policy.py",
+                        "reinforcement_learning/strategy.py",
                         "reinforcement_learning/agent.py",
                         "reinforcement_learning/environment.py",
                         "reinforcement_learning/replay_memory.py",
-                        "reinforcement_learning/util.py"
+                        "reinforcement_learning/util.py",
+                        "reinforcement_learning/cnnarch_logger.py"
                 )
         );
     }
@@ -262,11 +263,12 @@ public class GenerationTest extends AbstractSymtabTest {
                         "reward/pylib/armanpy/armanpy_3d.i",
                         "reward/pylib/armanpy/numpy.i",
                         "reinforcement_learning/__init__.py",
-                        "reinforcement_learning/action_policy.py",
+                        "reinforcement_learning/strategy.py",
                         "reinforcement_learning/agent.py",
                         "reinforcement_learning/environment.py",
                         "reinforcement_learning/replay_memory.py",
                         "reinforcement_learning/util.py",
+                        "reinforcement_learning/cnnarch_logger.py",
                         "reinforcement_learning/torcs_agent_dqn_reward_executor.py"
                 )
         );
@@ -292,5 +294,33 @@ public class GenerationTest extends AbstractSymtabTest {
         String[] args = {"-m", "src/test/resources/models/reinforcementModel", "-r", "mountaincar.Master", "-b", "GLUON", "-f", "n", "-c", "n"};
         EMADLGeneratorCli.main(args);
         assertEquals(0, Log.getFindings().stream().filter(Finding::isError).count());
+
+        checkFilesAreEqual(
+                Paths.get("./target/generated-sources-emadl"),
+                Paths.get("./src/test/resources/target_code/gluon/reinforcementModel/mountaincar"),
+                Arrays.asList(
+                        "mountaincar_master.cpp",
+                        "mountaincar_master.h",
+                        "mountaincar_master_actor.h",
+                        "CMakeLists.txt",
+                        "CNNBufferFile.h",
+                        "CNNCreator_mountaincar_master_actor.py",
+                        "CNNNet_mountaincar_master_actor.py",
+                        "CNNPredictor_mountaincar_master_actor.h",
+                        "CNNTrainer_mountaincar_master_actor.py",
+                        "CNNTranslator.h",
+                        "HelperA.h",
+                        "start_training.sh",
+                        "reinforcement_learning/__init__.py",
+                        "reinforcement_learning/CNNCreator_MountaincarCritic.py",
+                        "reinforcement_learning/CNNNet_MountaincarCritic.py",
+                        "reinforcement_learning/strategy.py",
+                        "reinforcement_learning/agent.py",
+                        "reinforcement_learning/environment.py",
+                        "reinforcement_learning/replay_memory.py",
+                        "reinforcement_learning/util.py",
+                        "reinforcement_learning/cnnarch_logger.py"
+                )
+        );
     }
 }

src/test/resources/models/reinforcementModel/cartpole/agent/CartPoleDQN.cnnt

@@ -24,7 +24,7 @@ configuration CartPoleDQN {
         sample_size : 32
     }
 
-    action_selection : epsgreedy{
+    strategy : epsgreedy{
         epsilon : 1.0
         min_epsilon : 0.01
         epsilon_decay_method: linear

src/test/resources/models/reinforcementModel/mountaincar/agent/MountaincarActor.cnnt

@@ -14,21 +14,26 @@ configuration MountaincarActor {
 
     snapshot_interval : 20
 
-    loss : euclidean
-
     replay_memory : buffer{
-        memory_size : 10000
-        sample_size : 32
+        memory_size : 1000000
+        sample_size : 64
     }
 
-    action_selection : epsgreedy{
+    strategy : ornstein_uhlenbeck{
         epsilon : 1.0
         min_epsilon : 0.01
         epsilon_decay_method: linear
         epsilon_decay : 0.01
+        mu: (0.0)
+        theta: (0.15)
+        sigma: (0.3)
+    }
+
+    actor_optimizer : adam {
+        learning_rate : 0.0001
     }
 
-    optimizer : rmsprop{
+    critic_optimizer : adam {
         learning_rate : 0.001
     }
 }
\ No newline at end of file

src/test/resources/models/reinforcementModel/mountaincar/agent/MountaincarCritic.cnna

@@ -8,8 +8,5 @@ implementation Critic(state, action) {
         FullyConnected(units=300)
     ) ->
     Add() ->
-    Relu() ->
-    FullyConnected(units=1) ->
-    Tanh() ->
-    critic
+    Relu()
 }
\ No newline at end of file

src/test/resources/models/reinforcementModel/torcs/agent/dqn/TorcsDQN.cnnt

@@ -30,7 +30,7 @@ configuration TorcsDQN {
         sample_size : 32
     }
 
-    action_selection : epsgreedy{
+    strategy : epsgreedy{
         epsilon : 1.0
         min_epsilon : 0.01
         epsilon_decay_method: linear

src/test/resources/target_code/gluon/reinforcementModel/cartpole/CNNDataLoader_cartpole_master_dqn.py

+import os
+import h5py
+import mxnet as mx
+import logging
+import sys
+
+class cartpole_master_dqnDataLoader:
+    _input_names_ = ['state']
+    _output_names_ = ['qvalues_label']
+
+    def __init__(self):
+        self._data_dir = "data/"
+
+    def load_data(self, batch_size):
+        train_h5, test_h5 = self.load_h5_files()
+
+        data_mean = train_h5[self._input_names_[0]][:].mean(axis=0)
+        data_std = train_h5[self._input_names_[0]][:].std(axis=0) + 1e-5
+
+        train_iter = mx.io.NDArrayIter(train_h5[self._input_names_[0]],
+                                       train_h5[self._output_names_[0]],
+                                       batch_size=batch_size,
+                                       data_name=self._input_names_[0],
+                                       label_name=self._output_names_[0])
+        test_iter = None
+        if test_h5 != None:
+            test_iter = mx.io.NDArrayIter(test_h5[self._input_names_[0]],
+                                          test_h5[self._output_names_[0]],
+                                          batch_size=batch_size,
+                                          data_name=self._input_names_[0],
+                                          label_name=self._output_names_[0])
+        return train_iter, test_iter, data_mean, data_std
+
+    def load_h5_files(self):
+        train_h5 = None
+        test_h5 = None
+        train_path = self._data_dir + "train.h5"
+        test_path = self._data_dir + "test.h5"
+        if os.path.isfile(train_path):
+            train_h5 = h5py.File(train_path, 'r')
+            if not (self._input_names_[0] in train_h5 and self._output_names_[0] in train_h5):
+                logging.error("The HDF5 file '" + os.path.abspath(train_path) + "' has to contain the datasets: "
+                              + "'" + self._input_names_[0] + "', '" + self._output_names_[0] + "'")
+                sys.exit(1)
+            test_iter = None
+            if os.path.isfile(test_path):
+                test_h5 = h5py.File(test_path, 'r')
+                if not (self._input_names_[0] in test_h5 and self._output_names_[0] in test_h5):
+                    logging.error("The HDF5 file '" + os.path.abspath(test_path) + "' has to contain the datasets: "
+                                  + "'" + self._input_names_[0] + "', '" + self._output_names_[0] + "'")
+                    sys.exit(1)
+            else:
+                logging.warning("Couldn't load test set. File '" + os.path.abspath(test_path) + "' does not exist.")
+            return train_h5, test_h5
+        else:
+            logging.error("Data loading failure. File '" + os.path.abspath(train_path) + "' does not exist.")
+            sys.exit(1)
\ No newline at end of file

src/test/resources/target_code/gluon/reinforcementModel/cartpole/CNNTrainer_cartpole_master_dqn.py

 from reinforcement_learning.agent import DqnAgent
 from reinforcement_learning.util import AgentSignalHandler
+from reinforcement_learning.cnnarch_logger import ArchLogger
 import reinforcement_learning.environment
 import CNNCreator_cartpole_master_dqn
 
@@ -9,9 +10,6 @@ import re
 import logging
 import mxnet as mx
 
-session_output_dir = 'session'
-agent_name='cartpole_master_dqn'
-session_param_output = os.path.join(session_output_dir, agent_name)
 
 def resume_session():
     session_param_output = os.path.join(session_output_dir, agent_name)
@@ -32,60 +30,73 @@ def resume_session():
                 break
     return resume_session, resume_directory
 
+
 if __name__ == "__main__":
+    agent_name='cartpole_master_dqn'
+    # Prepare output directory and logger
+    output_directory = 'model_output'\
+        + '/' + agent_name\
+        + '/' + time.strftime(
+            '%Y-%m-%d-%H-%M-%S', time.localtime(time.time()))
+    ArchLogger.set_output_directory(output_directory)
+    ArchLogger.set_logger_name(agent_name)
+    ArchLogger.set_output_level(ArchLogger.INFO)
+
     env = reinforcement_learning.environment.GymEnvironment('CartPole-v0')
-    context = mx.cpu()
-    net_creator = CNNCreator_cartpole_master_dqn.CNNCreator_cartpole_master_dqn()
-    net_creator.construct(context)
 
-    replay_memory_params = {
-        'method':'buffer',
-        'memory_size':10000,
-        'sample_size':32,
-        'state_dtype':'float32',
-        'action_dtype':'uint8',
-        'rewards_dtype':'float32'
-    }
+    context = mx.cpu()
+    qnet_creator = CNNCreator_cartpole_master_dqn.CNNCreator_cartpole_master_dqn()
+    qnet_creator.construct(context)
 
-    policy_params = {
-        'method':'epsgreedy',
-        'epsilon': 1,
-        'min_epsilon': 0.01,
-        'epsilon_decay_method': 'linear',
-        'epsilon_decay': 0.01,
+    agent_params = {
+        'environment': env,
+        'replay_memory_params': {
+            'method':'buffer',
+            'memory_size':10000,
+            'sample_size':32,
+            'state_dtype':'float32',
+            'action_dtype':'float32',
+            'rewards_dtype':'float32'
+        },
+        'strategy_params': {
+            'method':'epsgreedy',
+            'epsilon': 1,
+            'min_epsilon': 0.01,
+            'epsilon_decay_method': 'linear',
+            'epsilon_decay': 0.01,
+        },
+        'agent_name': agent_name,
+        'verbose': True,
+        'state_dim': (4,),
+        'action_dim': (2,),
+        'ctx': 'cpu',
+        'discount_factor': 0.999,
+        'training_episodes': 160,
+        'train_interval': 1,
+        'snapshot_interval': 20,
+        'max_episode_step': 250,
+        'target_score': 185.5,
+        'qnet':qnet_creator.net,
+        'use_fix_target': True,
+        'target_update_interval': 200,
+        'loss_function': 'euclidean',
+        'optimizer': 'rmsprop',
+        'optimizer_params': {
+            'learning_rate': 0.001        },
+        'double_dqn': False,
     }
 
-    resume_session, resume_directory = resume_session()
+    resume, resume_directory = resume_session()
 
-    if resume_session:
-        agent = DqnAgent.resume_from_session(resume_directory, net_creator.net, env)
+    if resume:
+        resume_agent_params = {
+            'session_dir': resume_directory,
+            'environment': env,
+            'net': qnet_creator.net,
+        }
+        agent = DqnAgent.resume_from_session(**resume_agent_params)
     else:
-        agent = DqnAgent(
-            network = net_creator.net,
-            environment=env,
-            replay_memory_params=replay_memory_params,
-            policy_params=policy_params,
-            state_dim=net_creator.get_input_shapes()[0],
-            ctx='cpu',
-            discount_factor=0.999,
-            loss_function='euclidean',
-            optimizer='rmsprop',
-            optimizer_params={
-                'learning_rate': 0.001            },
-            training_episodes=160,
-            train_interval=1,
-            use_fix_target=True,
-            target_update_interval=200,
-            double_dqn = False,
-            snapshot_interval=20,
-            agent_name=agent_name,
-            max_episode_step=250,
-            output_directory=session_output_dir,
-            verbose=True,
-            live_plot = True,
-            make_logfile=True,
-            target_score=185.5
-        )
+        agent = DqnAgent(**agent_params)
 
     signal_handler = AgentSignalHandler()
     signal_handler.register_agent(agent)
@@ -93,4 +104,4 @@ if __name__ == "__main__":
     train_successful = agent.train()
 
     if train_successful:
-        agent.save_best_network(net_creator._model_dir_ + net_creator._model_prefix_ + '_newest', epoch=0)
\ No newline at end of file
+        agent.save_best_network(qnet_creator._model_dir_ + qnet_creator._model_prefix_ + '_newest', epoch=0)

src/test/resources/target_code/gluon/reinforcementModel/cartpole/reinforcement_learning/action_policy.py

-import numpy as np
-
-class ActionPolicyBuilder(object):
-    def __init__(self):
-        pass
-
-    def build_by_params(self,
-        method='epsgreedy',
-        epsilon=0.5,
-        min_epsilon=0.05,
-        epsilon_decay_method='no',
-        epsilon_decay=0.0,
-        action_dim=None):
-
-        if epsilon_decay_method == 'linear':
-            decay = LinearDecay(eps_decay=epsilon_decay, min_eps=min_epsilon)
-        else:
-            decay = NoDecay()
-
-        if method == 'epsgreedy':
-            assert action_dim is not None
-            assert len(action_dim) == 1
-            return EpsilonGreedyActionPolicy(eps=epsilon,
-                number_of_actions=action_dim[0], decay=decay)
-        else:
-            assert action_dim is not None
-            assert len(action_dim) == 1
-            return GreedyActionPolicy()
-
-class EpsilonGreedyActionPolicy(object):
-    def __init__(self, eps, number_of_actions, decay):
-        self.eps = eps
-        self.cur_eps = eps
-        self.__number_of_actions = number_of_actions
-        self.__decay_method = decay
-
-    def select_action(self, values):
-        do_exploration = (np.random.rand() < self.cur_eps)
-        if do_exploration:
-            action = np.random.randint(low=0, high=self.__number_of_actions)
-        else:
-            action = values.asnumpy().argmax()
-        return action
-
-    def decay(self):
-        self.cur_eps = self.__decay_method.decay(self.cur_eps)
-
-
-class GreedyActionPolicy(object):
-    def __init__(self):
-        pass
-
-    def select_action(self, values):
-        return values.asnumpy().argmax()
-
-    def decay(self):
-        pass
-
-
-class NoDecay(object):
-    def __init__(self):
-        pass
-
-    def decay(self, cur_eps):
-        return cur_eps
-
-class LinearDecay(object):
-    def __init__(self, eps_decay, min_eps=0):
-        self.eps_decay = eps_decay
-        self.min_eps = min_eps
-
-    def decay(self, cur_eps):
-        return max(cur_eps - self.eps_decay, self.min_eps)
\ No newline at end of file

src/test/resources/target_code/gluon/reinforcementModel/cartpole/reinforcement_learning/cnnarch_logger.py

+import logging
+import sys
+import os
+import util
+
+
+class ArchLogger(object):
+    _logger = None
+
+    __output_level = logging.INFO
+    __logger_name = 'agent'
+    __output_directory = '.'
+    __append = True
+    __logformat = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+    __dateformat = '%d-%b-%y %H:%M:%S'
+
+    INFO = logging.INFO
+    DEBUG = logging.DEBUG
+
+    @staticmethod
+    def set_output_level(output_level):
+        assert output_level is not None
+        ArchLogger.__output_level = output_level
+
+    @staticmethod
+    def set_logger_name(logger_name):
+        assert logger_name is not None
+        ArchLogger.__logger_name = logger_name
+
+    @staticmethod
+    def set_output_directory(output_directory):
+        assert output_directory is not None
+        ArchLogger.__output_directory = output_directory
+
+    @staticmethod
+    def set_append(append):
+        assert append is not None
+        ArchLogger.__append = append
+
+    @staticmethod
+    def set_log_format(logformat, dateformat):
+        assert logformat is not None
+        assert dateformat is not None
+        ArchLogger.__logformat = logformat
+        ArchLogger.__dateformat = dateformat
+
+    @staticmethod
+    def init_logger(make_log_file=True):
+        assert ArchLogger._logger is None, 'Logger init already called'
+        filemode = 'a' if ArchLogger.__append else 'w'
+        formatter = logging.Formatter(
+            fmt=ArchLogger.__logformat, datefmt=ArchLogger.__dateformat)
+
+        logger = logging.getLogger(ArchLogger.__logger_name)
+        logger.setLevel(ArchLogger.__output_level)
+
+        stream_handler = logging.StreamHandler(sys.stdout)
+        stream_handler.setLevel(ArchLogger.__output_level)
+        stream_handler.setFormatter(formatter)
+        logger.addHandler(stream_handler)
+
+        if make_log_file:
+            util.make_directory_if_not_exist(ArchLogger.__output_directory)
+            log_file = os.path.join(
+                ArchLogger.__output_directory,
+                ArchLogger.__logger_name + '.log')
+            file_handler = logging.FileHandler(log_file, mode=filemode)
+            file_handler.setLevel(ArchLogger.__output_level)
+            file_handler.setFormatter(formatter)
+            logger.addHandler(file_handler)
+        ArchLogger._logger = logger
+
+    @staticmethod
+    def get_logger():
+        if ArchLogger._logger is None:
+            ArchLogger.init_logger()
+        assert ArchLogger._logger is not None
+        return ArchLogger._logger
+
+if __name__ == "__main__":
+    print('=== Test logger ===')
+    ArchLogger.set_logger_name('TestLogger')
+    ArchLogger.set_output_directory('test_log')
+    ArchLogger.init_logger()
+    logger = ArchLogger.get_logger()
+    logger.warning('This is a warning')
+    logger.debug('This is a debug information, which you should not see')
+    logger.info('This is a normal information')
+    assert os.path.exists('test_log')\
+        and os.path.isfile(os.path.join('test_log', 'TestLogger.log')),\
+        'Test failed: No logfile exists'
+    import shutil
+    shutil.rmtree('test_log')
\ No newline at end of file

src/test/resources/target_code/gluon/reinforcementModel/cartpole/reinforcement_learning/environment.py

@@ -33,13 +33,6 @@ class GymEnvironment(Environment):
     def state_dim(self):
         return self.__env.observation_space.shape
 
-    @property
-    def state_dtype(self):
-        return 'float32'
-
-    @property
-    def action_dtype(self):
-        return 'uint8'
 
     @property
     def number_of_actions(self):

src/test/resources/target_code/gluon/reinforcementModel/cartpole/reinforcement_learning/replay_memory.py

 import numpy as np
 
+
 class ReplayMemoryBuilder(object):
     def __init__(self):
         self.__supported_methods = ['online', 'buffer', 'combined']
 
-    def build_by_params(self,
+    def build_by_params(
+        self,
         state_dim,
         method='online',
         state_dtype='float32',
+        action_dim=(1,),
         action_dtype='uint8',
         rewards_dtype='float32',
         memory_size=1000,
-        sample_size=32):
+        sample_size=32
+    ):
         assert state_dim is not None
+        assert action_dim is not None
         assert method in self.__supported_methods
 
         if method == 'online':
-            return self.build_online_memory(state_dim=state_dim, state_dtype=state_dtype,
-                action_dtype=action_dtype, rewards_dtype=rewards_dtype)
+            return self.build_online_memory(
+                state_dim=state_dim, state_dtype=state_dtype,
+                action_dtype=action_dtype, action_dim=action_dim,
+                rewards_dtype=rewards_dtype)
         else:
             assert memory_size is not None and memory_size > 0
             assert sample_size is not None and sample_size > 0
             if method == 'buffer':
-                return self.build_buffered_memory(state_dim=state_dim, sample_size=sample_size,
-                    memory_size=memory_size, state_dtype=state_dtype, action_dtype=action_dtype,
+                return self.build_buffered_memory(
+                    state_dim=state_dim, sample_size=sample_size,
+                    memory_size=memory_size, state_dtype=state_dtype,
+                    action_dim=action_dim, action_dtype=action_dtype,
                     rewards_dtype=rewards_dtype)