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/tests/architectures/mxnet_components/embedders/test_image_embedder.py

@@ -0,0 +1,21 @@
+import mxnet as mx
+import os
+import pytest
+import sys
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
+
+
+from rl_coach.base_parameters import EmbedderScheme
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
+from rl_coach.architectures.mxnet_components.embedders.image_embedder import ImageEmbedder
+
+
+@pytest.mark.unit_test
+def test_image_embedder():
+    params = InputEmbedderParameters(scheme=EmbedderScheme.Medium)
+    emb = ImageEmbedder(params=params)
+    emb.initialize()
+    input_data = mx.nd.random.uniform(low=0, high=1, shape=(10, 3, 244, 244))
+    output = emb(input_data)
+    assert len(output.shape) == 2  # since last block was flatten
+    assert output.shape[0] == 10  # since batch_size is 10

rl_coach/tests/architectures/mxnet_components/embedders/test_vector_embedder.py

@@ -0,0 +1,22 @@
+import mxnet as mx
+import os
+import pytest
+import sys
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
+
+
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
+from rl_coach.architectures.mxnet_components.embedders.vector_embedder import VectorEmbedder
+from rl_coach.base_parameters import EmbedderScheme
+
+
+@pytest.mark.unit_test
+def test_vector_embedder():
+    params = InputEmbedderParameters(scheme=EmbedderScheme.Medium)
+    emb = VectorEmbedder(params=params)
+    emb.initialize()
+    input_data = mx.nd.random.uniform(low=0, high=255, shape=(10, 100))
+    output = emb(input_data)
+    assert len(output.shape) == 2  # since last block was flatten
+    assert output.shape[0] == 10  # since batch_size is 10
+    assert output.shape[1] == 256  # since last dense layer has 256 units