more cleaning

environments/dataset_fusioner.py

@@ -10,22 +10,23 @@
 
 
 def main():
-    parser = argparse.ArgumentParser(description='Dataset Manipulator: useful to fusion two datasets by concatenating '
-                                                 'episodes. PS: Deleting sources after fusion into destination folder.')
+    parser = argparse.ArgumentParser(description='Dataset Manipulator: useful to merge two datasets by concatenating '
+                                                 + 'episodes. PS: Deleting sources after merging into the destination '
+                                                 + 'folder.')
     group = parser.add_mutually_exclusive_group()
     group.add_argument('--merge', type=str, nargs=3, metavar=('source_1', 'source_2', 'destination'),
                        default=argparse.SUPPRESS,
-                       help='Fusion two datasets by appending the episodes, deleting sources right after.')
+                       help='Merge two datasets by appending the episodes, deleting sources right after.')
 
     args = parser.parse_args()
 
     if 'merge' in args:
         # let make sure everything is in order
         assert os.path.exists(args.merge[0]), "Error: dataset '{}' could not be found".format(args.merge[0])
-        assert (not os.path.exists(args.merge[2])), "Error: dataset '{}' already exists, cannot rename '{}' to '{}'"\
-                                                          .format(args.merge[2], args.merge[0], args.merge[2])
+        assert (not os.path.exists(args.merge[2])), \
+            "Error: dataset '{}' already exists, cannot rename '{}' to '{}'".format(args.merge[2], args.merge[0],
+                                                                                    args.merge[2])
         # create the output
-        print(args)
         os.mkdir(args.merge[2])
 
         # copy files from first source

environments/dataset_generator.py

@@ -14,6 +14,7 @@
 
 from environments import ThreadingType
 from environments.registry import registered_env
+from real_robots.constants import USING_OMNIROBOT
 from srl_zoo.utils import printRed, printYellow
 
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'  # used to remove debug info of tensorflow
@@ -61,6 +62,7 @@ def env_thread(args, thread_num, partition=True, use_ppo2=False):
 
     env_class = registered_env[args.env][0]
     env = env_class(**env_kwargs)
+    using_real_omnibot = args.env == "OmnirobotEnv-v0" and USING_OMNIROBOT
 
     model = None
     if use_ppo2:
@@ -92,7 +94,9 @@ def env_thread(args, thread_num, partition=True, use_ppo2=False):
             if use_ppo2:
                 action, _ = model.predict([obs])
             else:
-                if episode_toward_target_on and np.random.rand() < args.toward_target_timesteps_proportion:
+                # Using a target reaching policy (untrained, from camera) when collecting data from real OmniRobot
+                if episode_toward_target_on and np.random.rand() < args.toward_target_timesteps_proportion and \
+                        using_real_omnibot:
                     action = [env.actionPolicyTowardTarget()]
                 else:
                     action = [env.action_space.sample()]
@@ -103,7 +107,7 @@ def env_thread(args, thread_num, partition=True, use_ppo2=False):
             frames += 1
             t += 1
             if done:
-                if np.random.rand() < args.toward_target_timesteps_proportion:
+                if np.random.rand() < args.toward_target_timesteps_proportion and using_real_omnibot:
                     episode_toward_target_on = True
                 else:
                     episode_toward_target_on = False

real_robots/omnirobot_utils/marker_finder.py

@@ -16,15 +16,15 @@ def rotateMatrix90(matrix):
     return new_matrix
 
 
-def hammingDistance(s1, s2):
+def hammingDistance(string_1, string_2):
     """
 
-    :param s1: (str)
-    :param s2: (str)
-    :return: (int) Hamming distance between s1 & s2
+    :param string_1: (str)
+    :param string_2: (str)
+    :return: (int) Hamming distance between string_1 & string_2
     """
-    assert len(s1) == len(s2)
-    return sum(ch1 != ch2 for ch1, ch2 in zip(s1, s2))
+    assert len(string_1) == len(string_2)
+    return sum(ch1 != ch2 for ch1, ch2 in zip(string_1, string_2))
 
 
 class MakerFinder():

real_robots/omnirobot_utils/omnirobot_manager_base.py

@@ -4,7 +4,7 @@
 
 
 class OmnirobotManagerBase(object):
-    def __init__(self, second_cam_topic=None):
+    def __init__(self):
         """
         This class is the basic class for omnirobot server, and omnirobot simulator's server.
         This class takes omnirobot position at instant t, and takes the action at instant t,

rl_baselines/base_classes.py

@@ -137,9 +137,10 @@ def save(self, save_path, _locals=None):
         }
         with open(save_path, "wb") as f:
             pickle.dump(save_param, f)
+
     def setLoadPath(self, load_path):
         """
-        Load the only the parameters of the neuro-network model from a path
+        Set the path to later load the parameters of a trained rl model
         :param load_path: (str)
         :return: None
         """
@@ -237,12 +238,12 @@ def train(self, args, callback, env_kwargs=None, train_kwargs=None):
         self.ob_space = envs.observation_space
         self.ac_space = envs.action_space
 
-        policy_fn = {'cnn': CnnPolicy,
-                     'cnn-lstm': CnnLstmPolicy,
-                     'cnn-lnlstm': CnnLnLstmPolicy,
-                     'mlp': MlpPolicy,
-                     'lstm': MlpLstmPolicy,
-                     'lnlstm': MlpLnLstmPolicy}[args.policy]
+        policy_fn = {'cnn': "CnnPolicy",
+                     'cnn-lstm': "CnnLstmPolicy",
+                     'cnn-lnlstm': "CnnLnLstmPolicy",
+                     'mlp': "MlpPolicy",
+                     'lstm': "MlpLstmPolicy",
+                     'lnlstm': "MlpLnLstmPolicy"}[args.policy]
         if self.load_rl_model_path is not None:
             print("Load trained model from the path: ", self.load_rl_model_path)
             self.model = self.model_class.load(self.load_rl_model_path, envs, **train_kwargs)