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/mxnet_components/embedders/vector_embedder.py

@@ -0,0 +1,71 @@
+from typing import Union
+from types import ModuleType
+
+import mxnet as mx
+from mxnet import nd, sym
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
+from rl_coach.architectures.mxnet_components.embedders.embedder import InputEmbedder
+from rl_coach.architectures.mxnet_components.layers import Dense
+from rl_coach.base_parameters import EmbedderScheme
+
+nd_sym_type = Union[mx.nd.NDArray, mx.sym.Symbol]
+
+
+class VectorEmbedder(InputEmbedder):
+    def __init__(self, params: InputEmbedderParameters):
+        """
+        An vector embedder is an input embedder that takes an vector input from the state and produces a vector
+        embedding by passing it through a neural network.
+
+        :param params: parameters object containing input_clipping, input_rescaling, batchnorm, activation_function
+            and dropout properties.
+        """
+        super(VectorEmbedder, self).__init__(params)
+        self.input_rescaling = params.input_rescaling['vector']
+        self.input_offset = params.input_offset['vector']
+
+    @property
+    def schemes(self):
+        """
+        Schemes are the pre-defined network architectures of various depths and complexities that can be used. Are used
+        to create Block when VectorEmbedder is initialised.
+
+        :return: dictionary of schemes, with key of type EmbedderScheme enum and value being list of mxnet.gluon.Block.
+        """
+        return {
+            EmbedderScheme.Empty:
+                [],
+
+            EmbedderScheme.Shallow:
+                [
+                    Dense(units=128)
+                ],
+
+            # Use for DQN
+            EmbedderScheme.Medium:
+                [
+                    Dense(units=256)
+                ],
+
+            # Use for Carla
+            EmbedderScheme.Deep:
+                [
+                    Dense(units=128),
+                    Dense(units=128),
+                    Dense(units=128)
+                ]
+        }
+
+    def hybrid_forward(self, F: ModuleType, x: nd_sym_type, *args, **kwargs) -> nd_sym_type:
+        """
+        Used for forward pass through embedder network.
+
+        :param F: backend api, either `nd` or `sym` (if block has been hybridized).
+        :type F: nd or sym
+        :param x: vector representing environment state, of shape (batch_size, in_channels).
+        :return: embedding of environment state, of shape (batch_size, channels).
+        """
+        if isinstance(x, nd.NDArray) and len(x.shape) != 2 and self.scheme != EmbedderScheme.Empty:
+            raise ValueError("Vector embedders expect the input size to have 2 dimensions. The given size is: {}"
+                             .format(x.shape))
+        return super(VectorEmbedder, self).hybrid_forward(F, x, *args, **kwargs)