Add tensor input type for arbitrary dimensional observation (#125)

* Allow arbitrary dimensional observation (non vector or image)
* Added creating PlanarMapsObservationSpace to GymEnvironment when number of channels is not 1 or 3

rl_coach/architectures/tensorflow_components/embedders/__init__.py

@@ -1,4 +1,5 @@
 from .image_embedder import ImageEmbedder
 from .vector_embedder import VectorEmbedder
+from .tensor_embedder import TensorEmbedder
 
-__all__ = ['ImageEmbedder', 'VectorEmbedder']
+__all__ = ['ImageEmbedder', 'VectorEmbedder', 'TensorEmbedder']

rl_coach/architectures/tensorflow_components/embedders/tensor_embedder.py

@@ -0,0 +1,52 @@
+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import List
+
+import tensorflow as tf
+
+from rl_coach.architectures.tensorflow_components.layers import Conv2d, Dense
+from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedder
+from rl_coach.base_parameters import EmbedderScheme
+from rl_coach.core_types import InputTensorEmbedding
+
+
+class TensorEmbedder(InputEmbedder):
+    """
+    A tensor embedder is an input embedder that takes a tensor with arbitrary dimension and produces a vector
+    embedding by passing it through a neural network. An example is video data or 3D image data (i.e. 4D tensors)
+    or other type of data that is more than 1 dimension (i.e. not vector) but is not an image.
+
+    NOTE: There are no pre-defined schemes for tensor embedder. User must define a custom scheme by passing
+    a callable object as InputEmbedderParameters.scheme when defining the respective preset. This callable
+    object must accept a single input, the normalized observation, and return a Tensorflow symbol which
+    will calculate an embedding vector for each sample in the batch.
+    Keep in mind that the scheme is a list of Tensorflow symbols, which are stacked by optional batchnorm,
+    activation, and dropout in between as specified in InputEmbedderParameters.
+    """
+
+    def __init__(self, input_size: List[int], activation_function=tf.nn.relu,
+                 scheme: EmbedderScheme=None, batchnorm: bool=False, dropout_rate: float=0.0,
+                 name: str= "embedder", input_rescaling: float=1.0, input_offset: float=0.0, input_clipping=None,
+                 dense_layer=Dense, is_training=False):
+        super().__init__(input_size, activation_function, scheme, batchnorm, dropout_rate, name, input_rescaling,
+                         input_offset, input_clipping, dense_layer=dense_layer, is_training=is_training)
+        self.return_type = InputTensorEmbedding
+        assert scheme is not None, "Custom scheme (a list of callables) must be specified for TensorEmbedder"
+
+    @property
+    def schemes(self):
+        return {}

rl_coach/architectures/tensorflow_components/general_network.py

@@ -27,7 +27,7 @@ from rl_coach.architectures.tensorflow_components.architecture import TensorFlow
 from rl_coach.architectures.tensorflow_components import utils
 from rl_coach.base_parameters import AgentParameters, EmbeddingMergerType
 from rl_coach.core_types import PredictionType
-from rl_coach.spaces import SpacesDefinition, PlanarMapsObservationSpace
+from rl_coach.spaces import SpacesDefinition, PlanarMapsObservationSpace, TensorObservationSpace
 from rl_coach.utils import get_all_subclasses, dynamic_import_and_instantiate_module_from_params, indent_string
 
 
@@ -116,10 +116,12 @@ class GeneralTensorFlowNetwork(TensorFlowArchitecture):
             raise ValueError("The key for the input embedder ({}) must match one of the following keys: {}"
                              .format(input_name, allowed_inputs.keys()))
 
-        mod_names = {'image': 'ImageEmbedder', 'vector': 'VectorEmbedder'}
+        mod_names = {'image': 'ImageEmbedder', 'vector': 'VectorEmbedder', 'tensor': 'TensorEmbedder'}
 
         emb_type = "vector"
-        if isinstance(allowed_inputs[input_name], PlanarMapsObservationSpace):
+        if isinstance(allowed_inputs[input_name], TensorObservationSpace):
+            emb_type = "tensor"
+        elif isinstance(allowed_inputs[input_name], PlanarMapsObservationSpace):
             emb_type = "image"
 
         embedder_path = 'rl_coach.architectures.tensorflow_components.embedders:' + mod_names[emb_type]