improving reloading of the agents

l2rpn_baselines/DuelQSimple/train.py

@@ -115,7 +115,7 @@ def train(env,
         sizes = [800, 800, 800, 494, 494, 494]  # sizes of each hidden layers
         kwargs_archi = {'observation_size': observation_size,
                         'sizes': sizes,
-                        'activs': ["relu" for _ in range(sizes)],  # all relu activation function
+                        'activs': ["relu" for _ in sizes],  # all relu activation function
                         "list_attr_obs": li_attr_obs_X}
 
         # select some part of the action

l2rpn_baselines/SAC/SAC_NN.py

@@ -16,7 +16,6 @@
 with warnings.catch_warnings():
     warnings.filterwarnings("ignore", category=FutureWarning)
     from tensorflow.keras.models import load_model, Sequential, Model
-    import tensorflow.keras.optimizers as tfko
     from tensorflow.keras.layers import Activation, Dense
     from tensorflow.keras.layers import Input, Concatenate
 
@@ -245,11 +244,12 @@ def load_network(self, path, name=None, ext="h5"):
         We load all the models using the keras "load_model" function.
         """
         path_model, path_target_model, path_modelQ, path_modelQ2, path_policy = self._get_path_model(path, name)
-        self.model_value = load_model('{}.{}'.format(path_model, ext))
-        self.model_value_target = load_model('{}.{}'.format(path_target_model, ext))
-        self.model_Q = load_model('{}.{}'.format(path_modelQ, ext))
-        self.model_Q2 = load_model('{}.{}'.format(path_modelQ2, ext))
-        self.model_policy = load_model('{}.{}'.format(path_policy, ext))
+        self.construct_q_network()
+        self.model_value.load_weights('{}.{}'.format(path_model, ext))
+        self.model_value_target.load_weights('{}.{}'.format(path_target_model, ext))
+        self.model_Q.load_weights('{}.{}'.format(path_modelQ, ext))
+        self.model_Q2.load_weights('{}.{}'.format(path_modelQ2, ext))
+        self.model_policy.load_weights('{}.{}'.format(path_policy, ext))
         if self.verbose:
             print("Succesfully loaded network.")
 

l2rpn_baselines/utils/BaseDeepQ.py

@@ -13,8 +13,6 @@
 import tensorflow as tf
 import tensorflow.keras.optimizers as tfko
 
-from tensorflow.keras.models import load_model
-
 from l2rpn_baselines.utils.TrainingParam import TrainingParam
 
 
@@ -204,11 +202,14 @@ def load_network(self, path, name=None, ext="h5"):
             The file extension (by default h5)
         """
         path_model, path_target_model = self.get_path_model(path, name)
-        self._model = load_model('{}.{}'.format(path_model, ext), custom_objects=self._custom_objects)
+        # fix for issue https://github.com/keras-team/keras/issues/7440
+        self.construct_q_network()
+
+        self._model.load_weights('{}.{}'.format(path_model, ext))
 
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore")
-            self._target_model = load_model('{}.{}'.format(path_target_model, ext), custom_objects=self._custom_objects)
+            self._target_model.load_weights('{}.{}'.format(path_target_model, ext))
         if self.verbose:
             print("Succesfully loaded network.")