Adding mxnet components to rl_coach/architectures (#60)

Adding mxnet components to rl_coach architectures.

- Supports PPO and DQN
- Tested with CartPole_PPO and CarPole_DQN
- Normalizing filters don't work right now (see #49) and are disabled in CartPole_PPO preset
- Checkpointing is disabled for MXNet

rl_coach/architectures/tensorflow_components/architecture.py

@@ -13,8 +13,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
-import time
 import os
+import time
+from typing import Any, List, Tuple, Dict
 
 import numpy as np
 import tensorflow as tf
@@ -544,6 +545,26 @@ class TensorFlowArchitecture(Architecture):
             output = squeeze_list(output)
         return output
 
+    @staticmethod
+    def parallel_predict(sess: Any,
+                         network_input_tuples: List[Tuple['TensorFlowArchitecture', Dict[str, np.ndarray]]]) ->\
+            List[np.ndarray]:
+        """
+        :param sess: active session to use for prediction
+        :param network_input_tuples: tuple of network and corresponding input
+        :return: list of outputs from all networks
+        """
+        feed_dict = {}
+        fetches = []
+
+        for network, input in network_input_tuples:
+            feed_dict.update(network.create_feed_dict(input))
+            fetches += network.outputs
+
+        outputs = sess.run(fetches, feed_dict)
+
+        return outputs
+
     def train_on_batch(self, inputs, targets, scaler=1., additional_fetches=None, importance_weights=None):
         """
         Given a batch of examples and targets, runs a forward pass & backward pass and then applies the gradients