Merge branch 'integrate-reinforcement-gluon' into annotate-architecture

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
 import reinforcement_learning.environment
-
 import CNNCreator_cartpole_master_dqn
+
+import os
+import sys
+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)
+    resume_session = False
+    resume_directory = None
+    if os.path.isdir(session_output_dir) and os.path.isdir(session_param_output):
+        regex = re.compile(r'\d\d\d\d-\d\d-\d\d-\d\d-\d\d')
+        dir_content = os.listdir(session_param_output)
+        session_files = filter(regex.search, dir_content)
+        session_files.sort(reverse=True)
+        for d in session_files:
+            interrupted_session_dir = os.path.join(session_param_output, d, '.interrupted_session')
+            if os.path.isdir(interrupted_session_dir):
+                resume = raw_input('Interrupted session from {} found. Do you want to resume? (y/n) '.format(d))
+                if resume == 'y':
+                    resume_session = True
+                    resume_directory = interrupted_session_dir
+                break
+    return resume_session, resume_directory
+
 if __name__ == "__main__":
     env = reinforcement_learning.environment.GymEnvironment('CartPole-v0')
     context = mx.cpu()
@@ -28,32 +55,42 @@ if __name__ == "__main__":
         'epsilon_decay': 0.01,
     }
 
-    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='cartpole_master_dqn',
-        max_episode_step=250,
-        output_directory='model',
-        verbose=True,
-        live_plot = True,
-        make_logfile=True,
-        target_score=185.5
-    )
-    train_successfull = agent.train()
-    agent.save_best_network(net_creator._model_dir_ + net_creator._model_prefix_ + '_newest', epoch=0)
+    resume_session, resume_directory = resume_session()
+
+    if resume_session:
+        agent = DqnAgent.resume_from_session(resume_directory, net_creator.net, env)
+    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,
\ No newline at end of file
+            target_score=185.5
+        )
+
+    signal_handler = AgentSignalHandler()
+    signal_handler.register_agent(agent)
+
+    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

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

@@ -100,7 +100,7 @@ class DqnAgent(object):
         # Prepare output directory and logger
         self.__output_directory = output_directory\
             + '/' + self.__agent_name\
-            + '/' + time.strftime('%d-%m-%Y-%H-%M-%S', time.localtime(self.__creation_time))
+            + '/' + time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime(self.__creation_time))
         self.__logger = self.__setup_logging()
         self.__logger.info('Agent created with following parameters: {}'.format(self.__make_config_dict()))
 
@@ -113,9 +113,8 @@ class DqnAgent(object):
         return cls(network, environment, ctx=ctx, **config_dict)
 
     @classmethod
-    def resume_from_session(cls, session_dir, network_type):
+    def resume_from_session(cls, session_dir, net, environment):
         import pickle
-        session_dir = os.path.join(session_dir, '.interrupted_session')
         if not os.path.exists(session_dir):
             raise ValueError('Session directory does not exist')
 
@@ -132,13 +131,14 @@ class DqnAgent(object):
         with open(files['agent'], 'rb') as f:
             agent = pickle.load(f)
 
-        agent.__qnet = network_type()
+        agent.__environment = environment
+        agent.__qnet = net
         agent.__qnet.load_parameters(files['q_net_params'], agent.__ctx)
         agent.__qnet.hybridize()
-        agent.__qnet(nd.ones((1,) + agent.__environment.state_dim))
-        agent.__best_net = network_type()
+        agent.__qnet(nd.random_normal(shape=((1,) + agent.__state_dim), ctx=agent.__ctx))
+        agent.__best_net = copy_net(agent.__qnet, agent.__state_dim, agent.__ctx)
         agent.__best_net.load_parameters(files['best_net_params'], agent.__ctx)
-        agent.__target_qnet = network_type()
+        agent.__target_qnet = copy_net(agent.__qnet, agent.__state_dim, agent.__ctx)
         agent.__target_qnet.load_parameters(files['target_net_params'], agent.__ctx)
 
         agent.__logger = agent.__setup_logging(append=True)
@@ -157,6 +157,8 @@ class DqnAgent(object):
         del self.__training_stats.logger
         logger = self.__logger
         self.__logger = None
+        self.__environment.close()
+        self.__environment = None
 
         self.__save_net(self.__qnet, 'qnet', session_dir)
         self.__qnet = None
@@ -169,7 +171,7 @@ class DqnAgent(object):
 
         with open(agent_session_file, 'wb') as f:
             pickle.dump(self, f)
-
+        self.__logger = logger
         logger.info('State successfully stored')
 
     @property
@@ -293,7 +295,7 @@ class DqnAgent(object):
         return loss
 
     def __do_snapshot_if_in_interval(self, episode):
-        do_snapshot = (episode % self.__snapshot_interval == 0)
+        do_snapshot = (episode != 0 and (episode % self.__snapshot_interval == 0))
         if do_snapshot:
             self.save_parameters(episode=episode)
             self.__evaluate()
@@ -318,6 +320,7 @@ class DqnAgent(object):
 
         # Implementation Deep Q Learning described by Mnih et. al. in Playing Atari with Deep Reinforcement Learning
         while self.__current_episode < episodes:
+            # Check interrupt flag
             if self.__interrupt_flag:
                 self.__interrupt_flag = False
                 self.__interrupt_training()

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

@@ -17,6 +17,10 @@ class Environment:
     def step(self, action):
         pass
 
+    @abc.abstractmethod
+    def close(self):
+        pass
+
 import gym
 class GymEnvironment(Environment):
     def __init__(self, env_name, **kwargs):

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

@@ -37,13 +37,19 @@ class AgentSignalHandler(object):
     def __init__(self):
         signal.signal(signal.SIGINT, self.interrupt_training)
         self.__agent = None
+        self.__times_interrupted = 0
 
     def register_agent(self, agent):
         self.__agent = agent
 
     def interrupt_training(self, sig, frame):
-        if self.__agent:
-            self.__agent.set_interrupt_flag(True)
+        self.__times_interrupted = self.__times_interrupted + 1
+        if self.__times_interrupted <= 3:
+            if self.__agent:
+                self.__agent.set_interrupt_flag(True)
+        else:
+            print('Interrupt called three times: Force quit')
+            sys.exit(1)
 
 style.use('fivethirtyeight')
 class TrainingStats(object):

src/test/resources/target_code/gluon/reinforcementModel/torcs/CNNTrainer_torcs_agent_torcsAgent_dqn.py

 from reinforcement_learning.agent import DqnAgent
+from reinforcement_learning.util import AgentSignalHandler
 import reinforcement_learning.environment
-
 import CNNCreator_torcs_agent_torcsAgent_dqn
+
+import os
+import sys
+import re
 import logging
 import mxnet as mx
 
+session_output_dir = 'session'
+agent_name='torcs_agent_torcsAgent_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)
+    resume_session = False
+    resume_directory = None
+    if os.path.isdir(session_output_dir) and os.path.isdir(session_param_output):
+        regex = re.compile(r'\d\d\d\d-\d\d-\d\d-\d\d-\d\d')
+        dir_content = os.listdir(session_param_output)
+        session_files = filter(regex.search, dir_content)
+        session_files.sort(reverse=True)
+        for d in session_files:
+            interrupted_session_dir = os.path.join(session_param_output, d, '.interrupted_session')
+            if os.path.isdir(interrupted_session_dir):
+                resume = raw_input('Interrupted session from {} found. Do you want to resume? (y/n) '.format(d))
+                if resume == 'y':
+                    resume_session = True
+                    resume_directory = interrupted_session_dir
+                break
+    return resume_session, resume_directory
+
 if __name__ == "__main__":
     env_params = {
         'ros_node_name' : 'torcs_agent_torcsAgent_dqnTrainerNode',
@@ -35,32 +62,42 @@ if __name__ == "__main__":
         'epsilon_decay': 0.0001,
     }
 
-    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=20000,
-        train_interval=1,
-        use_fix_target=True,
-        target_update_interval=500,
-        double_dqn = True,
-        snapshot_interval=1000,
-        agent_name='torcs_agent_torcsAgent_dqn',
-        max_episode_step=999999999,
-        output_directory='model',
-        verbose=True,
-        live_plot = True,
-        make_logfile=True,
-        target_score=None
-    )
-    train_successfull = agent.train()
-    agent.save_best_network(net_creator._model_dir_ + net_creator._model_prefix_ + '_newest', epoch=0)
+    resume_session, resume_directory = resume_session()
+
+    if resume_session:
+        agent = DqnAgent.resume_from_session(resume_directory, net_creator.net, env)
+    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=20000,
+            train_interval=1,
+            use_fix_target=True,
+            target_update_interval=500,
+            double_dqn = True,
+            snapshot_interval=1000,
+            agent_name=agent_name,
+            max_episode_step=999999999,
+            output_directory=session_output_dir,
+            verbose=True,
+            live_plot = True,
+            make_logfile=True,
\ No newline at end of file
+            target_score=None
+        )
+
+    signal_handler = AgentSignalHandler()
+    signal_handler.register_agent(agent)
+
+    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

src/test/resources/target_code/gluon/reinforcementModel/torcs/reinforcement_learning/agent.py

@@ -100,7 +100,7 @@ class DqnAgent(object):
         # Prepare output directory and logger
         self.__output_directory = output_directory\
             + '/' + self.__agent_name\
-            + '/' + time.strftime('%d-%m-%Y-%H-%M-%S', time.localtime(self.__creation_time))
+            + '/' + time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime(self.__creation_time))
         self.__logger = self.__setup_logging()
         self.__logger.info('Agent created with following parameters: {}'.format(self.__make_config_dict()))
 
@@ -113,9 +113,8 @@ class DqnAgent(object):
         return cls(network, environment, ctx=ctx, **config_dict)
 
     @classmethod
-    def resume_from_session(cls, session_dir, network_type):
+    def resume_from_session(cls, session_dir, net, environment):
         import pickle
-        session_dir = os.path.join(session_dir, '.interrupted_session')
         if not os.path.exists(session_dir):
             raise ValueError('Session directory does not exist')
 
@@ -132,13 +131,14 @@ class DqnAgent(object):
         with open(files['agent'], 'rb') as f:
             agent = pickle.load(f)
 
-        agent.__qnet = network_type()
+        agent.__environment = environment
+        agent.__qnet = net
         agent.__qnet.load_parameters(files['q_net_params'], agent.__ctx)
         agent.__qnet.hybridize()
-        agent.__qnet(nd.ones((1,) + agent.__environment.state_dim))
-        agent.__best_net = network_type()
+        agent.__qnet(nd.random_normal(shape=((1,) + agent.__state_dim), ctx=agent.__ctx))
+        agent.__best_net = copy_net(agent.__qnet, agent.__state_dim, agent.__ctx)
         agent.__best_net.load_parameters(files['best_net_params'], agent.__ctx)
-        agent.__target_qnet = network_type()
+        agent.__target_qnet = copy_net(agent.__qnet, agent.__state_dim, agent.__ctx)
         agent.__target_qnet.load_parameters(files['target_net_params'], agent.__ctx)
 
         agent.__logger = agent.__setup_logging(append=True)
@@ -157,6 +157,8 @@ class DqnAgent(object):
         del self.__training_stats.logger
         logger = self.__logger
         self.__logger = None
+        self.__environment.close()
+        self.__environment = None
 
         self.__save_net(self.__qnet, 'qnet', session_dir)
         self.__qnet = None
@@ -169,7 +171,7 @@ class DqnAgent(object):
 
         with open(agent_session_file, 'wb') as f:
             pickle.dump(self, f)
-
+        self.__logger = logger
         logger.info('State successfully stored')
 
     @property
@@ -293,7 +295,7 @@ class DqnAgent(object):
         return loss
 
     def __do_snapshot_if_in_interval(self, episode):
-        do_snapshot = (episode % self.__snapshot_interval == 0)
+        do_snapshot = (episode != 0 and (episode % self.__snapshot_interval == 0))
         if do_snapshot:
             self.save_parameters(episode=episode)
             self.__evaluate()
@@ -318,6 +320,7 @@ class DqnAgent(object):
 
         # Implementation Deep Q Learning described by Mnih et. al. in Playing Atari with Deep Reinforcement Learning
         while self.__current_episode < episodes:
+            # Check interrupt flag
             if self.__interrupt_flag:
                 self.__interrupt_flag = False
                 self.__interrupt_training()

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

@@ -32,6 +32,10 @@ class Environment:
     def step(self, action):
         pass
 
+    @abc.abstractmethod
+    def close(self):
+        pass
+
 import rospy
 import thread
 import numpy as np
@@ -83,7 +87,8 @@ class RosEnvironment(Environment):
         reset_message.data = True
         self.__waiting_for_state_update = True
         self.__reset_publisher.publish(reset_message)
-        self.__wait_for_new_state(self.__reset_publisher, reset_message)
+        while self.__last_received_terminal:
+            self.__wait_for_new_state(self.__reset_publisher, reset_message)
         return self.__last_received_state
 
     def step(self, action):
@@ -119,6 +124,9 @@ class RosEnvironment(Environment):
                     exit()
             time.sleep(100/1000)
 
+    def close(self):
+        rospy.signal_shutdown('Program ended!')
+
 
     def __state_callback(self, data):
         self.__last_received_state = np.array(data.data, dtype='double')

src/test/resources/target_code/gluon/reinforcementModel/torcs/reinforcement_learning/torcs_agent_dqn_reward_executor.py

 # This file was automatically generated by SWIG (http://www.swig.org).
-# Version 3.0.12
+# Version 3.0.8
 #
 # Do not make changes to this file unless you know what you are doing--modify
 # the SWIG interface file instead.
 
-from sys import version_info as _swig_python_version_info
-if _swig_python_version_info >= (2, 7, 0):
-    def swig_import_helper():
-        import importlib
-        pkg = __name__.rpartition('.')[0]
-        mname = '.'.join((pkg, '_torcs_agent_dqn_reward_executor')).lstrip('.')
-        try:
-            return importlib.import_module(mname)
-        except ImportError:
-            return 
+
+
+
+
+from sys import version_info
+if version_info >= (2, 6, 0):